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Chen Z, Cruciani L, Lievore E, Fontana M, De Cobelli O, Musi G, Ferrigno G, De Momi E. Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107937. [PMID: 38006707 DOI: 10.1016/j.cmpb.2023.107937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/18/2023] [Accepted: 11/19/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. METHODS In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. RESULTS Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. CONCLUSIONS Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.
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Affiliation(s)
- Ziyang Chen
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy.
| | - Laura Cruciani
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy
| | - Elena Lievore
- European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy
| | - Matteo Fontana
- European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy
| | - Ottavio De Cobelli
- European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy; University of Milan, Department of Oncology and Onco-haematology, Faculty of Medicine and Surgery, Milan, Italy
| | - Gennaro Musi
- European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy; University of Milan, Department of Oncology and Onco-haematology, Faculty of Medicine and Surgery, Milan, Italy
| | - Giancarlo Ferrigno
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy
| | - Elena De Momi
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, 20133, Italy; European Institute of Oncology, Department of Urology, IRCCS, Milan, 20141, Italy
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Saha S, Dutta S, Goswami B, Nandi D. ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images. Biomed Signal Process Control 2023; 85:104974. [PMID: 37122956 PMCID: PMC10121143 DOI: 10.1016/j.bspc.2023.104974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 04/01/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
An automatic method for qualitative and quantitative evaluation of chest Computed Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop an automated COVID-19 prediction framework using deep learning. We put forth a novel Deep Neural Network (DNN) composed of an attention-based dense U-Net with deep supervision for COVID-19 lung lesion segmentation from chest CT images. We incorporate dense U-Net where convolution kernel size 5×5 is used instead of 3×3. The dense and transition blocks are introduced to implement a densely connected network on each encoder level. Also, the attention mechanism is applied between the encoder, skip connection, and decoder. These are used to keep both the high and low-level features efficiently. The deep supervision mechanism creates secondary segmentation maps from the features. Deep supervision combines secondary supervision maps from various resolution levels and produces a better final segmentation map. The trained artificial DNN model takes the test data at its input and generates a prediction output for COVID-19 lesion segmentation. The proposed model has been applied to the MedSeg COVID-19 chest CT segmentation dataset. Data pre-processing methods help the training process and improve performance. We compare the performance of the proposed DNN model with state-of-the-art models by computing the well-known metrics: dice coefficient, Jaccard coefficient, accuracy, specificity, sensitivity, and precision. As a result, the proposed model outperforms the state-of-the-art models. This new model may be considered an efficient automated screening system for COVID-19 diagnosis and can potentially improve patient health care and management system.
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Affiliation(s)
- Sanjib Saha
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, 713209, West Bengal, India
- Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, 713206, West Bengal, India
| | - Subhadeep Dutta
- Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, 713206, West Bengal, India
| | - Biswarup Goswami
- Department of Respiratory Medicine, Health and Family Welfare, Government of West Bengal, Kolkata, 700091, West Bengal, India
| | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, 713209, West Bengal, India
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Sundarasekar R, Appathurai A. FMTM-feature-map-based transform model for brain image segmentation in tumor detection. NETWORK (BRISTOL, ENGLAND) 2023; 34:1-25. [PMID: 36514820 DOI: 10.1080/0954898x.2022.2110620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/14/2022] [Accepted: 08/02/2022] [Indexed: 06/17/2023]
Abstract
The segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles. This impacts the outcome of tumour detection, causing errors. For addressing this issue, a Feature-Map based Transform Model (FMTM) is introduced to focus on heterogeneous features of input picture to map differences and intensity based on transition Fourier. Unchecked machine learning is used for reliable characteristic map recognition in this mapping process. For the determination of severity and variability, the method of identification depends on symmetry and texture. Learning instances are taught to improve precision using predefined data sets, regardless of loss of labels. The process is recurring until the maximum precision of tumour detection is achieved in low convergence. In this research, FMTM has been applied to brain tumour segmentation to automatically extract feature representations and produce accurate and steady performance because of promising performance made by powerful transition Fourier methods. The suggested model's performance is shown by the metrics processing time, precision, accuracy, and F1-Score.
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Niu K, Guo Z, Peng X, Pei S. P-ResUnet: Segmentation of brain tissue with Purified Residual Unet. Comput Biol Med 2022; 151:106294. [PMID: 36435055 DOI: 10.1016/j.compbiomed.2022.106294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/14/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
Abstract
Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.
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Affiliation(s)
- Ke Niu
- Beijing Information Science and Technology University, Beijing, China.
| | - Zhongmin Guo
- Beijing Information Science and Technology University, Beijing, China.
| | - Xueping Peng
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
| | - Su Pei
- Beijing Information Science and Technology University, Beijing, China.
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Wu L, Hu S, Liu C. MR brain segmentation based on DE-ResUnet combining texture features and background knowledge. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8000781. [PMID: 35140806 PMCID: PMC8820931 DOI: 10.1155/2022/8000781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/15/2022] [Indexed: 12/05/2022]
Abstract
Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model.
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