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Zheng Q, Gao Y, Zhou M, Li H, Lin J, Zhang W, Chen X. Semi or fully automatic tooth segmentation in CBCT images: a review. PeerJ Comput Sci 2024; 10:e1994. [PMID: 38660190 PMCID: PMC11041986 DOI: 10.7717/peerj-cs.1994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.
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Affiliation(s)
- Qianhan Zheng
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Gao
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mengqi Zhou
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huimin Li
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Lin
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weifang Zhang
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Social Medicine & Health Affairs Administration, Zhejiang University, Hangzhou, China
| | - Xuepeng Chen
- Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Clinical Research Center for Oral Diseases of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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Liu Y, Yao S, Wang X, Chen J, Li X. MD- UNet: a medical image segmentation network based on mixed depthwise convolution. Med Biol Eng Comput 2024; 62:1201-1212. [PMID: 38158549 DOI: 10.1007/s11517-023-03005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/16/2023] [Indexed: 01/03/2024]
Abstract
In the process of cancer diagnosis and treatment, accurate extraction of the lesion area helps the doctor to judge the condition. Currently, medical image segmentation algorithms based on UNet have been verified to be able to play an important role in clinical diagnosis. However, these methods still have the following drawbacks in extracting the region of interest (ROI): (1) ignoring the intra-class variability of medical images. (2) Failure to obtain effective feature redundancy. To address these problems, a U-shaped medical image segmentation network based on a Mixed depthwise convolution residual module (MDRM), called MD-UNet, is proposed in this paper. In MD-UNet, the MDRM built with a Mixed depthwise convolution attention block (MDAB) captures both local and global dependencies in the image to mitigate the effects of intra-class differences. MDAB captures valid redundant features and further captures global features of the input data. At the same time, the lightweight MDAB senses changes in the receptive field and generates multiple feature mappings. Compared with UNeXt on the ISIC2018 dataset, the MD-UNet segmentation accuracy Dice and IoU are improved by 1.33% and 1.91%, respectively. The code is available at https://github.com/Cloud-Liu/MD-UNet .
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Affiliation(s)
- Yun Liu
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
| | - Shuanglong Yao
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
| | - Xing Wang
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
| | - Ji Chen
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
| | - Xiaole Li
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
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Yang Y, Wang P, Yang Z, Zeng Y, Chen F, Wang Z, Rizzo S. Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network. Transl Cancer Res 2024; 13:1567-1583. [PMID: 38617525 PMCID: PMC11009801 DOI: 10.21037/tcr-23-1858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 03/01/2024] [Indexed: 04/16/2024]
Abstract
Background Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma. Methods This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified. Results The outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma. Conclusions The method based on the improved UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block UNet, which can improve the incorporation of glioma segmentation into the clinical process.
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Affiliation(s)
- Yang Yang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Peng Wang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Zhenyu Yang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Yuecheng Zeng
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Feng Chen
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Zhiyong Wang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Stefania Rizzo
- Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
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Chen Y, Zhang X, Peng L, He Y, Sun F, Sun H. Medical image segmentation network based on multi-scale frequency domain filter. Neural Netw 2024; 175:106280. [PMID: 38579574 DOI: 10.1016/j.neunet.2024.106280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 02/15/2024] [Accepted: 03/27/2024] [Indexed: 04/07/2024]
Abstract
With the development of deep learning, medical image segmentation in computer-aided diagnosis has become a research hotspot. Recently, UNet and its variants have become the most powerful medical image segmentation methods. However, these methods suffer from (1) insufficient sensing field and insufficient depth; (2) computational nonlinearity and redundancy of channel features; and (3) ignoring the interrelationships among feature channels. These problems lead to poor network segmentation performance and weak generalization ability. Therefore, first of all, we propose an effective replacement scheme of UNet base block, Double residual depthwise atrous convolution (DRDAC) block, to effectively improve the deficiency of receptive field and depth. Secondly, a new linear module, the Multi-scale frequency domain filter (MFDF), is designed to capture global information from the frequency domain. The high order multi-scale relationship is extracted by combining the depthwise atrous separable convolution with the frequency domain filter. Finally, a channel attention called Axial selection channel attention (ASCA) is redesigned to enhance the network's ability to model feature channel interrelationships. Further, we design a novel frequency domain medical image segmentation baseline method FDFUNet based on the above modules. We conduct extensive experiments on five publicly available medical image datasets and demonstrate that the present method has stronger segmentation performance as well as generalization ability compared to other state-of-the-art baseline methods.
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Affiliation(s)
- Yufeng Chen
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, PR China.
| | - Xiaoqian Zhang
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, PR China.
| | - Lifan Peng
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, PR China.
| | - Youdong He
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, PR China.
| | - Feng Sun
- Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang 621010, China; NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, Mianyang 621010, PR China.
| | - Huaijiang Sun
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.
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Guilenea FN, Casciaro ME, Soulat G, Mousseaux E, Craiem D. Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images. Biomed Phys Eng Express 2024; 10:035007. [PMID: 38437732 DOI: 10.1088/2057-1976/ad2ff2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/04/2024] [Indexed: 03/06/2024]
Abstract
Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.
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Affiliation(s)
- Federico N Guilenea
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro-CONICET, Solís 453, Buenos Aires CP 1078, Argentina
| | - Mariano E Casciaro
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro-CONICET, Solís 453, Buenos Aires CP 1078, Argentina
| | - Gilles Soulat
- Cardiovascular Imaging Unit, Hôpital Européen Georges Pompidou, INSERM U970, 75015 Paris, France
| | - Elie Mousseaux
- Cardiovascular Imaging Unit, Hôpital Européen Georges Pompidou, INSERM U970, 75015 Paris, France
| | - Damian Craiem
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro-CONICET, Solís 453, Buenos Aires CP 1078, Argentina
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Xiong L, Li N, Qiu W, Luo Y, Li Y, Zhang Y. Re- UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction. Med Biol Eng Comput 2024; 62:701-712. [PMID: 37982956 DOI: 10.1007/s11517-023-02966-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/03/2023] [Indexed: 11/21/2023]
Abstract
In recent years, the growing awareness of public health has brought attention to low-dose computed tomography (LDCT) scans. However, the CT image generated in this way contains a lot of noise or artifacts, which make increasing researchers to investigate methods to enhance image quality. The advancement of deep learning technology has provided researchers with novel approaches to enhance the quality of LDCT images. In the past, numerous studies based on convolutional neural networks (CNN) have yielded remarkable results in LDCT image reconstruction. Nonetheless, they all tend to continue to design new networks based on the fixed network architecture of UNet shape, which also leads to more and more complex networks. In this paper, we proposed a novel network model with a reverse U-shape architecture for the noise reduction in the LDCT image reconstruction task. In the model, we further designed a novel multi-scale feature extractor and edge enhancement module that yields a positive impact on CT images to exhibit strong structural characteristics. Evaluated on a public dataset, the experimental results demonstrate that the proposed model outperforms the compared algorithms based on traditional U-shaped architecture in terms of preserving texture details and reducing noise, as demonstrated by achieving the highest PSNR, SSIM and RMSE value. This study may shed light on the reverse U-shaped network architecture for CT image reconstruction, and could investigate the potential on other medical image processing.
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Affiliation(s)
- Lianjin Xiong
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Ning Li
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Wei Qiu
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Yiqian Luo
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Yishi Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yangsong Zhang
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China.
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China.
- Key Laboratory of Testing Technology for Manufacturing Process, Ministry of Education, Southwest University of Science and Technology, Mianyang, 621010, China.
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Cao X, Zhang T, Tao C, Ren Y, Wang X. A new method: Characterize and quantify biofilm wrinkles by UNet and Sholl Analysis. Biosystems 2024; 237:105131. [PMID: 38286325 DOI: 10.1016/j.biosystems.2024.105131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 01/31/2024]
Abstract
The wrinkles on the biofilm contain a lot of information about biofilm growth, so it is essential to characterize and quantify these wrinkles from the original microscopic images to discover more rules governing the biofilm morphology evolution. However, the existing methods to extract the wrinkles are time-consuming, error-prone, and require manual calibration. We propose a new system: using a deep learning method - UNet to identify the biofilm wrinkles in the original experimental images, which can achieve fast and accurate extraction of wrinkles on biofilms. Combining the result of UNet and medical neuron analysis method - Sholl Analysis, we can easily characterize and quantity the B. subtilis biofilm wrinkles. We proposed new characterization parameters such as wrinkle density, wrinkle length, and wrinkle projection area, which can precisely partition the biofilm surface wrinkles into different regions from the biofilm center to the edge, different regions correspond to different growth stages. Our system can be applied to study biofilms growing in different kinds of environments and to study the biofilm growth mechanisms.
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Affiliation(s)
- Xiaolei Cao
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Tiecheng Zhang
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Cong Tao
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yifan Ren
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaoling Wang
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; School of Engineering and Applied Sciences, Harvard University, 02138, Cambridge, MA, USA.
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Zhang W, Chen S, Ma Y, Liu Y, Cao X. ET UNet:Exploring efficient transformer enhanced UNet for 3D brain tumor segmentation. Comput Biol Med 2024; 171:108005. [PMID: 38340437 DOI: 10.1016/j.compbiomed.2024.108005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 01/03/2024] [Accepted: 01/13/2024] [Indexed: 02/12/2024]
Abstract
Medical image segmentation is a crucial topic in medical image processing. Accurately segmenting brain tumor regions from multimodal MRI scans is essential for clinical diagnosis and survival prediction. However, similar intensity distributions, variable tumor shapes, and fuzzy boundaries pose severe challenges for brain tumor segmentation. Traditional segmentation networks based on UNet struggle to establish explicit long-range dependencies from the feature space due to the limitations of the CNN receptive field. This is particularly crucial for dense prediction tasks such as brain tumor segmentation. Recent works have incorporated the powerful global modeling capability of Transformer into UNet to achieve more precise segmentation results. Nevertheless, these methods encounter some issues: (1) the global information is often modeled by simply stacking Transformer layers for a specific module, resulting in high computational complexity and underutilization of the potential of the UNet architecture; (2) the rich boundary information of tumor subregions in multi-scale features is often overlooked. Motivated by these challenges, we propose an advanced fusion of Transformer with UNet by reexamining the core three parts (encoder, bottleneck, and skip connections). Firstly, we introduce a CNN-Transformer module in the encoder to replace the traditional CNN module, enabling the capture of deep spatial dependencies from input images. To address high-level semantic information, we incorporate a computationally efficient spatial-channel attention layer in the bottleneck for global interaction, highlighting important semantic features from the encoder path output. For irregular lesions, we fuse the multi-scale features from the encoder output and the decoder features in the skip connections by calculating cross-attention. This adaptive querying of valuable information from multi-scale features enhances the boundary localization ability of the decoder path and suppresses redundant features with low correlation. Compared to existing methods, our model further enhances the learning capacity of the overall UNet architecture while maintaining low computational complexity. Experimental results on the BraTS2018 and BraTS2020 datasets for brain tumor segmentation tasks demonstrate that our model achieves comparable or superior results compared to recent CNN or Transformer-based models. The average DSC and HD95 on the two datasets are 0.854, 6.688, and 0.862, 5.455 respectively. At the same time, our model achieves optimal segmentation of Enhancing tumors, showcasing the effectiveness of our method. Our code will be made publicly available at https://github.com/wzhangck/ETUnet.
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Affiliation(s)
- Wang Zhang
- School of Computer and Information Science, SouthWest University, China.
| | - Shanxiong Chen
- School of Computer and Information Science, SouthWest University, China.
| | - Yuqi Ma
- School of Computer and Information Science, SouthWest University, China.
| | - Yu Liu
- School of Electronic Information and Electrical Engineering, TianShui Normal University, China.
| | - Xu Cao
- Department of Radiology, Shifang People's Hospital, China.
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Khaledyan D, Marini TJ, O’Connell A, Meng S, Kan J, Brennan G, Zhao Y, Baran TM, Parker KJ. WAT UNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound. Mach Learn Sci Technol 2024; 5:015042. [PMID: 38464559 PMCID: PMC10921088 DOI: 10.1088/2632-2153/ad2e15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/31/2024] [Accepted: 02/28/2024] [Indexed: 03/12/2024] Open
Abstract
Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public 'Breast Ultrasound Images' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar's test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.
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Affiliation(s)
- Donya Khaledyan
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States of America
| | - Thomas J Marini
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Avice O’Connell
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Steven Meng
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Jonah Kan
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Galen Brennan
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Yu Zhao
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Timothy M Baran
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Kevin J Parker
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, United States of America
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Schmidt EK, Krishnan C, Onuoha E, Gregory AV, Kline TL, Mrug M, Cardenas C, Kim H. Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data. Clin Imaging 2024; 106:110068. [PMID: 38101228 DOI: 10.1016/j.clinimag.2023.110068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD). METHODS We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC). RESULTS The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1-2 % higher than those by the models trained with single institutional data (0.90-0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2-20 % higher than those by the models trained with single institutional data (0.66-0.86). CONCLUSION The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation.
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Affiliation(s)
- Emma K Schmidt
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Chetana Krishnan
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ezinwanne Onuoha
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | | | - Timothy L Kline
- Department of Radiology, Mayo Clinic, Rochester, MN 55902, USA
| | - Michal Mrug
- Department of Veterans Affairs Medical Center, Birmingham, AL 35233, USA; Department of Nephrology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Carlos Cardenas
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Harrison Kim
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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11
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Alabdulhafith M, Ba Mahel AS, Samee NA, Mahmoud NF, Talaat R, Muthanna MSA, Nassef TM. Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds. Front Med (Lausanne) 2024; 11:1310137. [PMID: 38357646 PMCID: PMC10865496 DOI: 10.3389/fmed.2024.1310137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024] Open
Abstract
Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.
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Affiliation(s)
- Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Abduljabbar S. Ba Mahel
- School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Rawan Talaat
- Biotechnology and Genetics Department, Agriculture Engineering, Ain Shams University, Cairo, Egypt
| | | | - Tamer M. Nassef
- Computer and Software Engineering Department, Engineering College, Misr University for Science and Technology, 6th of October, Egypt
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12
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Buongiorno R, Del Corso G, Germanese D, Colligiani L, Python L, Romei C, Colantonio S. Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models. J Imaging 2023; 9:283. [PMID: 38132701 PMCID: PMC10744014 DOI: 10.3390/jimaging9120283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.
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Affiliation(s)
- Rossana Buongiorno
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Giulio Del Corso
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Danila Germanese
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Leonardo Colligiani
- Department of Translational Research, Academic Radiology, University of Pisa, 56124 Pisa, PI, Italy;
| | - Lorenzo Python
- 2nd Radiology Unit, Pisa University Hospital, 56124 Pisa, PI, Italy; (L.P.)
| | - Chiara Romei
- 2nd Radiology Unit, Pisa University Hospital, 56124 Pisa, PI, Italy; (L.P.)
| | - Sara Colantonio
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
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13
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Marsilio L, Moglia A, Rossi M, Manzotti A, Mainardi L, Cerveri P. Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning. Bioengineering (Basel) 2023; 10:1433. [PMID: 38136024 PMCID: PMC10740423 DOI: 10.3390/bioengineering10121433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19-0.36) mm and 0.24 (0.18-0.32) mm, and of 1.06 (0.73-2.15) mm and 1.43 (0.82-2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline's efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.
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Affiliation(s)
- Luca Marsilio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.M.); (M.R.); (L.M.)
| | - Andrea Moglia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.M.); (M.R.); (L.M.)
| | - Matteo Rossi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.M.); (M.R.); (L.M.)
| | | | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.M.); (M.R.); (L.M.)
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (A.M.); (M.R.); (L.M.)
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14
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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15
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Zhang Z, Wu H, Zhao H, Shi Y, Wang J, Bai H, Sun B. A Novel Deep Learning Model for Medical Image Segmentation with Convolutional Neural Network and Transformer. Interdiscip Sci 2023; 15:663-677. [PMID: 37665496 DOI: 10.1007/s12539-023-00585-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/26/2023] [Accepted: 08/01/2023] [Indexed: 09/05/2023]
Abstract
Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that combine transformer and convolutional neural networks often use skip connections in U-shaped networks, which may limit their ability to capture contextual information in medical images. To address this limitation, we propose a coordinated mobile and residual transformer UNet (MRC-TransUNet) that combines the strengths of transformer and UNet architectures. Our approach uses a lightweight MR-ViT to address the semantic gap and a reciprocal attention module to compensate for the potential loss of details. To better explore long-range contextual information, we use skip connections only in the first layer and add MR-ViT and RPA modules in the subsequent downsampling layers. In our study, we evaluated the effectiveness of our proposed method on three different medical image segmentation datasets, namely, breast, brain, and lung. Our proposed method outperformed state-of-the-art methods in terms of various evaluation metrics, including the Dice coefficient and Hausdorff distance. These results demonstrate that our proposed method can significantly improve the accuracy of medical image segmentation and has the potential for clinical applications. Illustration of the proposed MRC-TransUNet. For the input medical images, we first subject them to an intrinsic downsampling operation and then replace the original jump connection structure using MR-ViT. The output feature representations at different scales are fused by the RPA module. Finally, an upsampling operation is performed to fuse the features to restore them to the same resolution as the input image.
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Affiliation(s)
- Zhuo Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China
| | - Hongbing Wu
- School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China
| | - Huan Zhao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China
| | - Yicheng Shi
- College of Management and Economics, Tianjin University, Tianjin, 300072, China
| | - Jifang Wang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China
| | - Hua Bai
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China.
| | - Baoshan Sun
- School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China.
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16
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Ali Z, Naz S, Yasmin S, Bukhari M, Kim M. Deep learning-assisted IoMT framework for cerebral microbleed detection. Heliyon 2023; 9:e22879. [PMID: 38125517 PMCID: PMC10731074 DOI: 10.1016/j.heliyon.2023.e22879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5-10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %. © 2017 Elsevier Inc. All rights reserved.
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Affiliation(s)
- Zeeshan Ali
- Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan
| | - Sheneela Naz
- Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Sadaf Yasmin
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan
| | - Maryam Bukhari
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan
| | - Mucheol Kim
- School of Computer Science and Engineering, Chung-Ang University, Seoul, 06974, South Korea
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17
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Sharma R, Tsiamyrtzis P, Webb AG, Leiss EL, Tsekos NV. Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI. MAGMA 2023:10.1007/s10334-023-01127-6. [PMID: 37989921 DOI: 10.1007/s10334-023-01127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/30/2023] [Accepted: 10/16/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVE This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics. MATERIALS AND METHODS To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters. RESULTS ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA. DISCUSSION These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.
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Affiliation(s)
- Rishabh Sharma
- Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA
| | - Panagiotis Tsiamyrtzis
- Department of Mechanical Engineering, Politecnico Di Milano, Milan, Italy
- Department of Statistics, Athens University of Economics and Business, Athens, Greece
| | - Andrew G Webb
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ernst L Leiss
- Department of Computer Science, University of Houston, Houston, TX, USA
| | - Nikolaos V Tsekos
- Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA.
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18
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AL Qurri A, Almekkawy M. Improved UNet with Attention for Medical Image Segmentation. Sensors (Basel) 2023; 23:8589. [PMID: 37896682 PMCID: PMC10611347 DOI: 10.3390/s23208589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/01/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer's long-range dependencies and CNNs' low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets.
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19
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Kaur G, Garg M, Gupta S, Juneja S, Rashid J, Gupta D, Shah A, Shaikh A. Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model. Diagnostics (Basel) 2023; 13:3152. [PMID: 37835895 PMCID: PMC10572820 DOI: 10.3390/diagnostics13193152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.
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Affiliation(s)
- Gurjinder Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Meenu Garg
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sapna Juneja
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Junaid Rashid
- Department of Data Science, Sejong University, Seoul 05006, Republic of Korea;
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Asadullah Shah
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia;
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20
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Yoganathan S, Aouadi S, Ahmed S, Paloor S, Torfeh T, Al-Hammadi N, Hammoud R. Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy. Phys Imaging Radiat Oncol 2023; 28:100512. [PMID: 38111501 PMCID: PMC10726231 DOI: 10.1016/j.phro.2023.100512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/20/2023] Open
Abstract
Background and purpose Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy. Materials and methods A novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation. ResUNet incorporates a self-attention mechanism in its long skip connections to enhance information transfer between the encoder and decoder. Data from 93 HN patients, each with planning CT (pCT) and first-day CBCT images were used. Model performance was evaluated using two DL approaches (non-adversarial and adversarial training) and two model types (2D axial only vs. 2.5D axial, sagittal, and coronal). ResUNet was compared with the traditional UNet through image quality assessment (Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM)) and dose calculation accuracy evaluation (DVH deviation and gamma evaluation (1 %/1mm)). Results Image similarity evaluation results for the 2.5D-ResUNet and 2.5D-UNet models were: MAE: 46±7 HU vs. 51±9 HU, PSNR: 66.6±2.0 dB vs. 65.8±1.8 dB, and SSIM: 0.81±0.04 vs. 0.79±0.05. There were no significant differences in dose calculation accuracy between DL models. Both models demonstrated DVH deviation below 0.5 % and a gamma-pass-rate (1 %/1mm) exceeding 97 %. Conclusions ResUNet enhanced CT number accuracy and image quality of sCT and outperformed UNet in sCT generation from CBCT. This method holds promise for generating precise sCT for HN ART.
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Affiliation(s)
- S.A. Yoganathan
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Souha Aouadi
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Sharib Ahmed
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Tarraf Torfeh
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
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21
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Fu L, Li S. A New Semantic Segmentation Framework Based on UNet. Sensors (Basel) 2023; 23:8123. [PMID: 37836953 PMCID: PMC10575066 DOI: 10.3390/s23198123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/17/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
This paper discusses a semantic segmentation framework and shows its application in agricultural intelligence, such as providing environmental awareness for agricultural robots to work autonomously and efficiently. We propose an ensemble framework based on the bagging strategy and the UNet network, using RGB and HSV color spaces. We evaluated the framework on our self-built dataset (Maize) and a public dataset (Sugar Beets). Then, we compared it with UNet-based methods (single RGB and single HSV), DeepLab V3+, and SegNet. Experimental results show that our ensemble framework can synthesize the advantages of each color space and obtain the best IoUs (0.8276 and 0.6972) on the datasets (Maize and Sugar Beets), respectively. In addition, including our framework, the UNet-based methods have faster speed and a smaller parameter space than DeepLab V3+ and SegNet, which are more suitable for deployment in resource-constrained environments such as mobile robots.
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Affiliation(s)
- Leiyang Fu
- School of Information & Computer Science, Anhui Agricultural University, Hefei 230036, China;
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Shaowen Li
- School of Information & Computer Science, Anhui Agricultural University, Hefei 230036, China;
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
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22
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Wang W, Qin D, Wang S, Fang Y, Zheng Y. A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation. Comput Biol Med 2023; 164:107282. [PMID: 37499297 DOI: 10.1016/j.compbiomed.2023.107282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 06/14/2023] [Accepted: 07/16/2023] [Indexed: 07/29/2023]
Abstract
Cardiopulmonary and cardiovascular diseases are fatal factors that threaten human health and cause many deaths worldwide each year, so it is essential to screen cardiopulmonary disease more accurately and efficiently. Auscultation is a non-invasive method for physicians' perception of the disease. The Heart Sounds (HS) and Lung Sounds (LS) recorded by an electronic stethoscope consist of acoustic information that is helpful in the diagnosis of pulmonary conditions. Still, inter-interference between HS and LS presented in both the time and frequency domains blocks diagnostic efficiency. This paper proposes a blind source separation (BSS)strategy that first classifies Heart-Lung-Sound (HLS) according to its LS features and then separates it into HS and LS. Sparse Non-negative Matrix Factorization (SNMF) is employed to extract the LS features in HLS, then proposed a network constructed by Dilated Convolutional Neural Network (DCNN) to classify HLS into five types by the magnitude features of LS. Finally, Multi-Channel UNet (MCUNet) separation model is utilized for each category of HLS. This paper is the first to propose the HLS classification method SNMF-DCNN and apply UNet to the cardiopulmonary sound separation domain. Compared with other state-of-the-art methods, the proposed framework in this paper has higher separation quality and robustness.
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Affiliation(s)
- Weibo Wang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China.
| | - Dimei Qin
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Shubo Wang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Yu Fang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Yongkang Zheng
- State Grid Sichuan Electric Power Research Institute, Chengdu, 610096, China
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23
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Yang E, Zhang H, Zang Z, Zhou Z, Wang S, Liu Z, Liu Y. GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction. Comput Biol Med 2023; 164:107246. [PMID: 37487383 DOI: 10.1016/j.compbiomed.2023.107246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023]
Abstract
RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.
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Affiliation(s)
- Enbin Yang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Hao Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China
| | - Zinan Zang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhiyong Zhou
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Shuo Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhen Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Graduate School of Engineering, Nagasaki Institute of Applied Science, 536 Aba-machi, Nagasaki 851-0193, Japan
| | - Yuanning Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China.
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24
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Liu H, Li Z, Lin S, Cheng L. A Residual UNet Denoising Network Based on Multi-Scale Feature Extraction and Attention-Guided Filter. Sensors (Basel) 2023; 23:7044. [PMID: 37631582 PMCID: PMC10459023 DOI: 10.3390/s23167044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/03/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023]
Abstract
In order to obtain high-quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention-guided filter and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also develop the attention-guided filter block to hold the edge information. Further, we use the global residual network strategy to model residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against several state-of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the sharpness of the image.
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Affiliation(s)
- Hualin Liu
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China; (H.L.); (S.L.); (L.C.)
- Laboratory of Remote Sensing Technology and Big Data Analysis, Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528437, China
| | - Zhe Li
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China; (H.L.); (S.L.); (L.C.)
- Laboratory of Remote Sensing Technology and Big Data Analysis, Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528437, China
| | - Shijie Lin
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China; (H.L.); (S.L.); (L.C.)
| | - Libo Cheng
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China; (H.L.); (S.L.); (L.C.)
- Laboratory of Remote Sensing Technology and Big Data Analysis, Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528437, China
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25
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Zhou P, Liu X, Xiong J. Skin lesion image segmentation based on lightweight multi-scale U-shaped network. Biomed Phys Eng Express 2023; 9:055021. [PMID: 37413980 DOI: 10.1088/2057-1976/ace4d0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023]
Abstract
UNet, and more recently medical image segmentation methods, utilize many parameters and computational quantities to achieve higher performance. However, due to the increasing demand for real-time medical image segmentation tasks, it is important to trade between accuracy rates and computational complexity. To this end, we propose a lightweight multi-scale U-shaped network (LMUNet), a multi-scale inverted residual and an asymmetric atrous spatial pyramid pooling-based network for skin lesion image segmentation. We test LMUNet on multiple medical image segmentation datasets, which show that it reduces the number of parameters by 67X and decreases the computational complexity by 48X while obtaining better performance over the partial lightweight networks.
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Affiliation(s)
- Pengfei Zhou
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
| | - Xuefeng Liu
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
| | - Jichuan Xiong
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, People's Republic of China
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26
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Saidani O, Aljrees T, Umer M, Alturki N, Alshardan A, Khan SW, Alsubai S, Ashraf I. Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features. Diagnostics (Basel) 2023; 13:2544. [PMID: 37568907 PMCID: PMC10417332 DOI: 10.3390/diagnostics13152544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.
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Affiliation(s)
- Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia; (O.S.); (N.A.); (A.A.)
| | - Turki Aljrees
- Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia;
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
| | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia; (O.S.); (N.A.); (A.A.)
| | - Amal Alshardan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia; (O.S.); (N.A.); (A.A.)
| | - Sardar Waqar Khan
- Department of Computer Science & Information Technology, The University of Lahore, Lahore 54000, Pakistan;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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27
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Khaledyan D, Marini TJ, O’Connell A, Parker K. Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet. bioRxiv 2023:2023.07.14.549040. [PMID: 37503223 PMCID: PMC10370074 DOI: 10.1101/2023.07.14.549040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it a popular choice among researchers in the medical image segmentation field. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the dice coefficient, specificity, sensitivity, and F1 score obtained values of 0.9283, 0.9936, 0.9426, and 0.9412, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperforms the earlier designed models and points towards improved breast lesion segmentation algorithms.
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Affiliation(s)
- Donya Khaledyan
- Department of Electrical and Electronics Engineering, University of Rochester, Rochester, NY, USA
| | - Thomas J. Marini
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Avice O’Connell
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Kevin Parker
- Department of Electrical and Electronics Engineering, University of Rochester, Rochester, NY, USA
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA
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28
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Kodipalli A, Fernandes SL, Gururaj V, Varada Rameshbabu S, Dasar S. Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:2282. [PMID: 37443676 DOI: 10.3390/diagnostics13132282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
Difficulty in detecting tumours in early stages is the major cause of mortalities in patients, despite the advancements in treatment and research regarding ovarian cancer. Deep learning algorithms were applied to serve the purpose as a diagnostic tool and applied to CT scan images of the ovarian region. The images went through a series of pre-processing techniques and, further, the tumour was segmented using the UNet model. The instances were then classified into two categories-benign and malignant tumours. Classification was performed using deep learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception, along with machine learning models such as Random Forest, Gradient Boosting, AdaBoosting and XGBoosting. DenseNet 121 emerges as the best model on this dataset after applying optimization on the machine learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures with common machine learning algorithms, with and without optimization techniques applied.
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Affiliation(s)
- Ashwini Kodipalli
- Department of Artificial Intelligence & Data Science, Global Academy of Technology, Bangalore 560098, India
| | - Steven L Fernandes
- Department of Computer Science, Design, Journalism, Creighton University, Omaha, NE 68178, USA
| | - Vaishnavi Gururaj
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Shriya Varada Rameshbabu
- Department of Computer Science & Engineering, Global Academy of Technology, Bangalore 560098, India
| | - Santosh Dasar
- Department of Radiologist, SDM College of Medical Sciences and Hospital, Dharwad 580009, India
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29
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Zhang Q, Yang Y, Liu G, Ning Y, Li J. Dairy Cow Mastitis Detection by Thermal Infrared Images Based on CLE- UNet. Animals (Basel) 2023; 13:2211. [PMID: 37444009 DOI: 10.3390/ani13132211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network's output to be closer to the position of the real label during the training process. Finally, we used a cow's eye ellipse fitting operation based on the similarity between the shape of the cow's eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis.
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Affiliation(s)
- Qian Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Ying Yang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Gang Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing 100083, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Yuanlin Ning
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jianquan Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
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30
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Zhang S, Niu Y. Lcm UNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation. Bioengineering (Basel) 2023; 10:712. [PMID: 37370643 DOI: 10.3390/bioengineering10060712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/26/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet's structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision.
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Affiliation(s)
- Shuai Zhang
- School of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
| | - Yanmin Niu
- School of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
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31
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Yang Y, Chen F, Liang H, Bai Y, Wang Z, Zhao L, Ma S, Niu Q, Li F, Xie T, Cai Y. CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors. Front Oncol 2023; 13:1166988. [PMID: 37333811 PMCID: PMC10272725 DOI: 10.3389/fonc.2023.1166988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Objective To investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis. Materials and methods From 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC). Results All seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively. Conclusion This retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary.
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Affiliation(s)
- Yin Yang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fei Chen
- Department of Pediatrics, Jiahui International Hospital, Shanghai, China
| | - Hongmei Liang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Wang
- School of Computer Science and Technology, Taiyuan Normal University, Taiyuan, China
| | - Lei Zhao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sai Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinghua Niu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianwu Xie
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Yingyu Cai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Zhang Z, Zhang X, Yang Y, Liu J, Zheng C, Bai H, Ma Q. Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-Trans UNet. Front Neurosci 2023; 17:1207149. [PMID: 37292160 PMCID: PMC10244508 DOI: 10.3389/fnins.2023.1207149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet.
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Affiliation(s)
- Zhuo Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Xiaochen Zhang
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, China
| | - Yong Yang
- School of Computer Science and Technology, Tiangong University, Tianjin, China
| | - Jieyu Liu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Chenzi Zheng
- College of Foreign Languages, Nankai University, Tianjin, China
| | - Hua Bai
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Quanfeng Ma
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, China
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Hong Y, Qiu Z, Chen H, Zhu B, Lei H. MAS- UNet: a U-shaped network for prostate segmentation. Front Med (Lausanne) 2023; 10:1190659. [PMID: 37275383 PMCID: PMC10232949 DOI: 10.3389/fmed.2023.1190659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/25/2023] [Indexed: 06/07/2023] Open
Abstract
Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region. Segmentation of the prostate region is of great significance for the diagnosis of prostate cancer. In the past, some methods have been used to segment the prostate region, but segmentation accuracy still has room for improvement. This study has proposed a new image segmentation model based on Attention UNet. The model improves Attention UNet by using GN instead of BN, adding dropout to prevent overfitting, introducing the ASPP module, adding channel attention to the attention gate module, and using different channels to output segmentation results of different prostate regions. Finally, we conducted comparative experiments using five existing UNet-based models, and used the dice coefficient as the metric to evaluate the segmentation result. The proposed model achieves dice scores of 0.807 and 0.907 in the transition region and the peripheral region, respectively. The experimental results show that the proposed model is better than other UNet-based models.
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Affiliation(s)
- YuQi Hong
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Zhao Qiu
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Huajing Chen
- Hainan Provincial Public Security Department, Haikou, China
| | - Bing Zhu
- Haikou Hospital of the Maternal and Child Health, Haikou, China
| | - Haodong Lei
- School of Computer Science and Technology, Hainan University, Haikou, China
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Redoloza FS, Williamson TN, Headman AO, Allred BJ. Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery. J Environ Qual 2023. [PMID: 37170699 DOI: 10.1002/jeq2.20493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 04/12/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023]
Abstract
Knowing sub-surface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time-series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008-2020. Training imagery were from agricultural areas within the U.S. Great Lakes basin. Validation imagery were from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93-96% and balanced accuracy of 52-54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library. This article is protected by copyright. All rights reserved.
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Affiliation(s)
| | | | | | - Barry J Allred
- U.S. Department of Agriculture, Agricultura Research Service - Soil Drainage Research Unit
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Su Y, Jia D, Shen Y, Wang L. Single-Channel Blind Image Separation Based on Transformer-Guided GAN. Sensors (Basel) 2023; 23:4638. [PMID: 37430553 DOI: 10.3390/s23104638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/06/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
Blind source separation (BSS) has been a great challenge in the field of signal processing due to the unknown distribution of the source signal and the mixing matrix. Traditional methods based on statistics and information theory use prior information such as source distribution independence, non-Gaussianity, sparsity, etc. to solve this problem. Generative adversarial networks (GANs) learn source distributions through games without being constrained by statistical properties. However, the current blind image separation methods based on GANs ignores the reconstruction of the structure and details of the separated image, resulting in residual interference source information in the generated results. This paper proposes a Transformer-guided GAN guided by an attention mechanism. Through the adversarial training of the generator and the discriminator, U-shaped Network (UNet) is used to fuse the convolutional layer features to reconstruct the structure of the separated image, and Transformer is used to calculate the position attention and guide the detailed information. We validate our method with quantitative experiments, showing that it outperforms previous blind image separation algorithms in terms of PSNR and SSIM.
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Affiliation(s)
- Yaya Su
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
| | - Dongli Jia
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
| | - Yankun Shen
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
| | - Lin Wang
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
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Abstract
BACKGROUND Coronary artery disease (CAD) manifests with a blockage the coronary arteries, usually due to plaque buildup, and has a serious impact on the human life. Atherosclerotic plaques, including fibrous plaques, lipid plaques, and calcified plaques can lead to occurrence of CAD. Optical coherence tomography (OCT) is employed in the clinical practice as it clearly provides a detailed display of the lesion plaques, thereby assessing the patient's condition. Analyzing the OCT images manually is a very tedious and time-consuming task for the clinicians. Therefore, automatic segmentation of the coronary OCT images is necessary. OBJECTIVE In view of the good utility of Unet network in the segmentation of medical images, the present study proposed the development of a Unet network based on Sk-Conv and spatial pyramid pooling modules to segment the coronary OCT images. METHODS In order to extract multi-scale features, these two modules were added at the bottom of UNet. Meanwhile, ablation experiments are designed to verify each module is effective. RESULTS After testing, our model achieves 0.8935 on f1 score and 0.7497 on mIOU. Compared to the current advanced models, our model performs better. CONCLUSION Our model achieves good results on OCT sequences.
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Affiliation(s)
- Zhan Wang
- School of Software, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jiawei Zheng
- Department of Cardiovascular Medicine, Second Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, Shaanxi, China
| | - Peilin Jiang
- School of Software, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Dengfeng Gao
- Department of Cardiovascular Medicine, Second Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, Shaanxi, China
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Marciniak T, Stankiewicz A, Zaradzki P. Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction. Sensors (Basel) 2023; 23:1870. [PMID: 36850467 PMCID: PMC9968084 DOI: 10.3390/s23041870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/31/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.
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38
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Luo S, Jiang H, Wang M. C 2BA- UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation. Comput Med Imaging Graph 2023; 103:102159. [PMID: 36549193 DOI: 10.1016/j.compmedimag.2022.102159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 11/11/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022]
Abstract
Tumor segmentation is a necessary step in clinical processing that can help doctors diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations and appearances vary substantially across individuals, and the contrast between tumors and adjacent normal tissues is low, tumor segmentation is still a challenging task. Although convolutional neural networks (CNNs) have achieved good results in tumor segmentation, the information about tumor boundaries has been rarely explored. To solve the problem, this paper proposes a new method for automatic tumor segmentation in PET/CT images based on context-coordination and boundary-aware, termed as C2BA-UNet. We employ a UNet-like backbone network and replace the encoder with EfficientNet-B0 for efficiency. To acquire potential tumor boundaries, we propose a new multi-atlas boundary-aware (MABA) module based on gradient atlas, uncertainty atlas, and level set atlas, that focuses on uncertain regions between tumors and adjacent tissues. Furthermore, we propose a new context coordination module (CCM) to combine multi-scale context information with attention mechanism to optimize skip connection in high-level layers. To validate the superiority of our method, we conduct experiments on a publicly available soft tissue sarcoma (STS) dataset and a lymphoma dataset, and the results show our method is competitive with other comparison methods.
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Affiliation(s)
- Shijie Luo
- Software College, Northeastern University, Shenyang 110819, China
| | - Huiyan Jiang
- Software College, Northeastern University, Shenyang 110819, China; Key Laboratory of Intelligent Computing in Biomedical Image, Ministry of Education, Northeastern University, Shenyang 110819, China.
| | - Meng Wang
- Software College, Northeastern University, Shenyang 110819, China
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Qiao Z, Du C. RAD- UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction. J Digit Imaging 2022; 35:1748-1758. [PMID: 35882689 PMCID: PMC9712860 DOI: 10.1007/s10278-022-00685-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 10/16/2022] Open
Abstract
To suppress the streak artifacts in images reconstructed from sparse-view projections in computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep network is proposed to achieve accurate sparse reconstruction. The filtered back projection (FBP) algorithm is used to reconstruct the CT image with streak artifacts from sparse-view projections. Then, the image is processed by the RAD-UNet to suppress streak artifacts and obtain high-quality CT image. Those images with streak artifacts are used as the input of the RAD-UNet, and the output-label images are the corresponding high-quality images. Through training via the large-scale training data, the RAD-UNet can obtain the capability of suppressing streak artifacts. This network combines residual connection, attention mechanism, dense connection and perceptual loss. This network can improve the nonlinear fitting capability and the performance of suppressing streak artifacts. The experimental results show that the RAD-UNet can improve the reconstruction accuracy compared with three existing representative deep networks. It may not only suppress streak artifacts but also better preserve image details. The proposed networks may be readily applied to other image processing tasks including image denoising, image deblurring, and image super-resolution.
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Affiliation(s)
- Zhiwei Qiao
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006 Shanxi China
| | - Congcong Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006 Shanxi China
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Huang X, Chen J, Chen M, Chen L, Wan Y. TDD- UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation. Comput Biol Med 2022; 151:106306. [PMID: 36403357 PMCID: PMC9664702 DOI: 10.1016/j.compbiomed.2022.106306] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods.
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Affiliation(s)
- Xuping Huang
- Computer School, University of South China, Hengyang 421001, China
| | - Junxi Chen
- Affiliated Nanhua Hospital, University of South China, Hengyang 421001, China
| | - Mingzhi Chen
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
| | - Lingna Chen
- Computer School, University of South China, Hengyang 421001, China,Corresponding author
| | - Yaping Wan
- Computer School, University of South China, Hengyang 421001, China,Corresponding author
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Wang M, Jiang H, Shi T, Wang Z, Guo J, Lu G, Wang Y, Yao YD. PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation. Comput Biol Med 2022; 151:106215. [PMID: 36306584 DOI: 10.1016/j.compbiomed.2022.106215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/04/2022] [Accepted: 10/15/2022] [Indexed: 12/27/2022]
Abstract
Lymphoma is a type of lymphatic tissue originated cancer. Automatic and accurate lymphoma segmentation is critical for its diagnosis and prognosis yet challenging due to the severely class-imbalanced problem. Generally, deep neural networks trained with class-observation-frequency based re-weighting loss functions are used to address this problem. However, the majority class can be under-weighted by them, due to the existence of data overlap. Besides, they are more mis-calibrated. To resolve these, we propose a neural network with prior-shift regularization (PSR-Net), which comprises a UNet-like backbone with re-weighting loss functions, and a prior-shift regularization (PSR) module including a prior-shift layer (PSL), a regularizer generation layer (RGL), and an expected prediction confidence updating layer (EPCUL). We first propose a trainable expected prediction confidence (EPC) for each class. Periodically, PSL shifts a prior training dataset to a more informative dataset based on EPCs; RGL presents a generalized informative-voxel-aware (GIVA) loss with EPCs and calculates it on the informative dataset for model finetuning in back-propagation; and EPCUL updates EPCs to refresh PSL and RRL in next forward-propagation. PSR-Net is trained in a two- stage manner. The backbone is first trained with re-weighting loss functions, then we reload the best saved model for the backbone and continue to train it with the weighted sum of the re-weighting loss functions, the GIVA regularizer and the L2 loss function of EPCs for regularization fine-tuning. Extensive experiments are performed based on PET/CT volumes with advanced stage lymphomas. Our PSR-Net achieves 95.12% sensitivity and 87.18% Dice coefficient, demonstrating the effectiveness of PSR-Net, when compared to the baselines and the state-of-the-arts.
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Affiliation(s)
- Meng Wang
- Department of Software College, Northeastern University, Shenyang 110819, China
| | - Huiyan Jiang
- Department of Software College, Northeastern University, Shenyang 110819, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, China.
| | - Tianyu Shi
- Department of Software College, Northeastern University, Shenyang 110819, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Military Area, Shenyang 110016, China
| | - Jia Guo
- Department of Nuclear Medicine, General Hospital of Northern Military Area, Shenyang 110016, China
| | - Guoxiu Lu
- Department of Nuclear Medicine, General Hospital of Northern Military Area, Shenyang 110016, China
| | - Youchao Wang
- Department of Nuclear Medicine, General Hospital of Northern Military Area, Shenyang 110016, China
| | - Yu-Dong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
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Bilimagga RS, Anchineyan P, Nmugam MS, Thalluri S, Goud PSK. Autodelineation of organ at risk in head and neck cancer radiotherapy using artificial intelligence. J Cancer Res Ther 2022; 18:S141-S145. [PMID: 36510954 DOI: 10.4103/jcrt.jcrt_1069_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Aim The aim of this study is to check the practical feasibility of artificial intelligence for day-to-day operations and how it generalizes when the data have considerable interobserver variability. Background Automated delineation of organ at risk (OAR) using a deep learning model is reasonably accurate. This will considerably reduce the medical professional time in manually contouring the OAR and also reduce the interobserver variation among radiation oncologists. It allows for quick radiation planning which helps in adaptive radiotherapy planning. Materials and Methods Head and neck (HN) computed tomography (CT) scan data of 113 patients were used in this study. CT scan was done as per the institute protocol. Each patient had about 100-300 slices in Dicom format. A total number of 19,240 images were used as the data set. The OARs were delineated by the radiation oncologist in the contouring system. Of the 113 patient records, 13 records were kept aside as test dataset and the remaining 100 records were used for training the UNet 2D model. The study was performed on the spinal cord and left and right parotids as OARs on HN CT images. The model performance was quantified using the Dice similarity coefficient (DSC) score. Results The trained model is used to predict three OARs, spinal cord and left and right parotids. The DSC score of 84% and above could be achieved using the UNet 2D Convolutional Neural Network. Conclusion This study showed that the accuracy of predicted organs was within acceptable DSC scores, even when the underlying dataset has significant interobserver variability.
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Affiliation(s)
- Ramesh S Bilimagga
- Department of Radiation Oncology, Healthcare Global Enterprises, Bangalore, Karnataka, India
| | - Pichandi Anchineyan
- Department of Radiation Oncology, Healthcare Global Enterprises, Bangalore, Karnataka, India
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Gomes R, Kamrowski C, Mohan PD, Senor C, Langlois J, Wildenberg J. Application of Deep Learning to IVC Filter Detection from CT Scans. Diagnostics (Basel) 2022; 12:diagnostics12102475. [PMID: 36292164 PMCID: PMC9600884 DOI: 10.3390/diagnostics12102475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
IVC filters (IVCF) perform an important function in select patients that have venous blood clots. However, they are usually intended to be temporary, and significant delay in removal can have negative health consequences for the patient. Currently, all Interventional Radiology (IR) practices are tasked with tracking patients in whom IVCF are placed. Due to their small size and location deep within the abdomen it is common for patients to forget that they have an IVCF. Therefore, there is a significant delay for a new healthcare provider to become aware of the presence of a filter. Patients may have an abdominopelvic CT scan for many reasons and, fortunately, IVCF are clearly visible on these scans. In this research a deep learning model capable of segmenting IVCF from CT scan slices along the axial plane is developed. The model achieved a Dice score of 0.82 for training over 372 CT scan slices. The segmentation model is then integrated with a prediction algorithm capable of flagging an entire CT scan as having IVCF. The prediction algorithm utilizing the segmentation model achieved a 92.22% accuracy at detecting IVCF in the scans.
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Affiliation(s)
- Rahul Gomes
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
- Correspondence: (R.G.); (J.W.)
| | - Connor Kamrowski
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
| | - Pavithra Devy Mohan
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
| | - Cameron Senor
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
| | - Jordan Langlois
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
| | - Joseph Wildenberg
- Interventional Radiology, Mayo Clinic Health System, Eau Claire, WI 54703, USA
- Correspondence: (R.G.); (J.W.)
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Manzanarez S, Manian V, Santos M. Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model. Sensors (Basel) 2022; 22:6895. [PMID: 36146242 PMCID: PMC9503776 DOI: 10.3390/s22186895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/25/2022] [Accepted: 09/01/2022] [Indexed: 06/16/2023]
Abstract
Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover image database, is created by citizen scientists worldwide who use their handheld cameras to take a set of six images per land cover site. These images have clutter due to man-made objects, and the pixel uncertainties result in incorrect labels. The problem of accurate labeling of these land cover images is addressed. An integrated architecture that combines Unet and DeepLabV3 for initial segmentation, followed by a weighted fusion model that combines the segmentation labels, is presented. The land cover images with labels are used for training the deep learning models. The fusion model combines the labels of five images taken from the north, south, east, west, and down directions to assign a unique label to the image sets. 2916 GLOBE images have been labeled with land cover classes using the integrated model with minimal human-in-the-loop annotation. The validation step shows that our architecture of labeling the images results in 90.97% label accuracy. Our fusion model can be used for labeling large databases of land cover classes from RGB images.
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Nillmani, Sharma N, Saba L, Khanna NN, Kalra MK, Fouda MM, Suri JS. Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans. Diagnostics (Basel) 2022; 12. [PMID: 36140533 DOI: 10.3390/diagnostics12092132] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/26/2022] [Accepted: 08/30/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.
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Presotto L, Bettinardi V, Bagnalasta M, Scifo P, Savi A, Vanoli EG, Fallanca F, Picchio M, Perani D, Gianolli L, De Bernardi E. Evaluation of a 2D UNet-Based Attenuation Correction Methodology for PET/MR Brain Studies. J Digit Imaging 2022; 35:432-445. [PMID: 35091873 PMCID: PMC9156597 DOI: 10.1007/s10278-021-00551-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 11/10/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 keV low-resolution attenuation maps therefore provide the best results in terms of both accuracy and variability. As for METH anatomically altered patients, DL properly reconstructs anatomical alterations. Quantitative results on PET images confirm those found on anatomically normal FDG patients.
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Affiliation(s)
- Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Bagnalasta
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Federico Fallanca
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy ,Vita-Salute San Raffaele University, Milan, Italy
| | - Daniela Perani
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy ,Vita-Salute San Raffaele University, Milan, Italy
| | - Luigi Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta De Bernardi
- School of Medicine and Surgery, University of Milano-Bicocca, via Cadore 48, Monza, 20900 Italy ,Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milan-Bicocca, Monza, Italy
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Chen S, Qiu C, Yang W, Zhang Z. Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation. Sensors (Basel) 2022; 22:3820. [PMID: 35632229 DOI: 10.3390/s22103820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 12/07/2022]
Abstract
The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore multiscale feature fusion, and there is still much room for improvement. In this paper, we propose a novel multiresolution aggregation transformer UNet (MRA-TUNet) based on multiscale input and coordinate attention for medical image segmentation. It realizes multiresolution aggregation from the following two aspects: (1) On the input side, a multiresolution aggregation module is used to fuse the input image information of different resolutions, which enhances the input features of the network. (2) On the output side, an output feature selection module is used to fuse the output information of different scales to better extract coarse-grained information and fine-grained information. We try to introduce a coordinate attention structure for the first time to further improve the segmentation performance. We compare with state-of-the-art medical image segmentation methods on the automated cardiac diagnosis challenge and the 2018 atrial segmentation challenge. Our method achieved average dice score of 0.911 for right ventricle (RV), 0.890 for myocardium (Myo), 0.961 for left ventricle (LV), and 0.923 for left atrium (LA). The experimental results on two datasets show that our method outperforms eight state-of-the-art medical image segmentation methods in dice score, precision, and recall.
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Cao T, Wang G, Ren L, Li Y, Wang H. Brain tumor magnetic resonance image segmentation by a multiscale contextual attention module combined with a deep residual UNet (MCA-ResUNet). Phys Med Biol 2022; 67. [PMID: 35294935 DOI: 10.1088/1361-6560/ac5e5c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/16/2022] [Indexed: 11/12/2022]
Abstract
Background and Objective. Automatic segmentation of MRI brain tumor area is a key step in the diagnosis and treatment of brain tumor. In recent years, the improved network based on UNet encoding and decoding structure has been widely used in brain tumor segmentation. However, due to continuous convolution and pooling operations, some spatial context information in existing networks will be discontinuous or even missing. It will affect the segmentation accuracy of the model. Therefore, the method proposed in this paper is to alleviate the lack of spatial context information and improve the accuracy of the model.Approach. This paper proposes a context attention module (multiscale contextual attention) to capture and filter out high-level features with spatial context information, which solves the problem of context information loss in feature extraction. The channel attention mechanism is introduced into the decoding structure to realize the fusion of high-level features and low-level features. The standard convolution block in the encoding and decoding structure is replaced by the pre-activated residual block to optimize the network training and improve the network performance.Results. This paper uses two public data sets (BraTs 2017 and BraTs 2019) to evaluate and verify the proposed method. Experimental results show that the proposed method can effectively alleviate the lack of spatial context information, and the segmentation performance is better than other existing methods.Significance. The method improves the segmentation performance of the model. It will assist doctors in making accurate diagnosis and provide reference basis for tumor resection. As a result, the proposed method will reduce the operation risk of patients and the postoperative recurrence rate.
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Affiliation(s)
- Tianyi Cao
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Guanglei Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Lili Ren
- The Affiliated Hospital of Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Yan Li
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, People's Republic of China
| | - Hongrui Wang
- Hebei University, Baoding, Hebei 071002, People's Republic of China
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Cai S, Wu Y, Chen G. A Novel Elastomeric UNet for Medical Image Segmentation. Front Aging Neurosci 2022; 14:841297. [PMID: 35360219 PMCID: PMC8961507 DOI: 10.3389/fnagi.2022.841297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Medical image segmentation is of important support for clinical medical applications. As most of the current medical image segmentation models are limited in the U-shaped structure, to some extent the deep convolutional neural network (CNN) structure design is hard to be accomplished. The design in this study mimics the way the wave is elastomeric propagating, extending the structure from both the horizontal and spatial dimensions for realizing the Elastomeric UNet (EUNet) structure. The EUNet can be divided into two types: horizontal EUNet and spatial EUNet, based on the propagation direction. The advantages of this design are threefold. First, the training structure can be deepened effectively. Second, the independence brought by each branch (a U-shaped design) makes the flexible design redundancy available. Finally, a horizontal and vertical series-parallel structure helps on feature accumulation and recursion. Researchers can adjust the design according to the requirements to achieve better segmentation performance for the independent structural design. The proposed networks were evaluated on two datasets: a self-built dataset (multi-photon microscopy, MPM) and publicly benchmark retinal datasets (DRIVE). The results of experiments demonstrated that the performance of EUNet outperformed the UNet and its variants.
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Affiliation(s)
- Sijing Cai
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
- School of Electronic & Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
| | - Yi Wu
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
- Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, China
| | - Guannan Chen
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
- Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, China
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Galli A, Marrone S, Piantadosi G, Sansone M, Sansone C. A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI. J Imaging 2021; 7:jimaging7120276. [PMID: 34940743 PMCID: PMC8703956 DOI: 10.3390/jimaging7120276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022] Open
Abstract
The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a "naive" use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new "Eras/Epochs" training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability.
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Affiliation(s)
- Antonio Galli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.G.); (M.S.); (C.S.)
| | - Stefano Marrone
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.G.); (M.S.); (C.S.)
- Correspondence:
| | - Gabriele Piantadosi
- Altran Italia S.p.A., Centro Direzionale, Via Giovanni Porzio, 4, 80143 Naples, Italy;
| | - Mario Sansone
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.G.); (M.S.); (C.S.)
| | - Carlo Sansone
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.G.); (M.S.); (C.S.)
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