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Ye Y, Luo Z, Qiu Z, Cao K, Huang B, Deng L, Zhang W, Liu G, Zou Y, Zhang J, Li J. Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation. Bioengineering (Basel) 2023; 10:1355. [PMID: 38135946 PMCID: PMC10740947 DOI: 10.3390/bioengineering10121355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.
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Affiliation(s)
- Yaojiang Ye
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Zixin Luo
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Zhengxuan Qiu
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Lei Deng
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;
| | - Guoqing Liu
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Yujian Zou
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Jian Zhang
- Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518060, China
| | - Jianpeng Li
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
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Chen M, Yi S, Yang M, Yang Z, Zhang X. UNet segmentation network of COVID-19 CT images with multi-scale attention. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16762-16785. [PMID: 37920033 DOI: 10.3934/mbe.2023747] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
In recent years, the global outbreak of COVID-19 has posed an extremely serious life-safety risk to humans, and in order to maximize the diagnostic efficiency of physicians, it is extremely valuable to investigate the methods of lesion segmentation in images of COVID-19. Aiming at the problems of existing deep learning models, such as low segmentation accuracy, poor model generalization performance, large model parameters and difficult deployment, we propose an UNet segmentation network integrating multi-scale attention for COVID-19 CT images. Specifically, the UNet network model is utilized as the base network, and the structure of multi-scale convolutional attention is proposed in the encoder stage to enhance the network's ability to capture multi-scale information. Second, a local channel attention module is proposed to extract spatial information by modeling local relationships to generate channel domain weights, to supplement detailed information about the target region to reduce information redundancy and to enhance important information. Moreover, the network model encoder segment uses the Meta-ACON activation function to avoid the overfitting phenomenon of the model and to improve the model's representational ability. A large number of experimental results on publicly available mixed data sets show that compared with the current mainstream image segmentation algorithms, the pro-posed method can more effectively improve the accuracy and generalization performance of COVID-19 lesions segmentation and provide help for medical diagnosis and analysis.
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Affiliation(s)
- Mingju Chen
- School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China
| | - Sihang Yi
- School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China
| | - Mei Yang
- Zigong Third People's Hospital, Zigong 643000, China
| | - Zhiwen Yang
- School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China
| | - Xingyue Zhang
- School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China
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Wong KKL, Xu W, Ayoub M, Fu YL, Xu H, Shi R, Zhang M, Su F, Huang Z, Chen W. Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107602. [PMID: 37244234 DOI: 10.1016/j.cmpb.2023.107602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional LSTM (BDC-LSTM) can exploit interlayer spatial dependencies, and HDC can expand the receptive domain without losing resolution. METHODS In this paper, we propose a segmentation method by combining BDC-LSTM and U-Net to segment the corpus callosum from multiple angles of brain images based on computed tomography (CT) and magnetic resonance imaging (MRI) in which two types of sequence, namely T2-weighted imaging as well as the Fluid Attenuated Inversion Recovery (Flair), were utilized. The two-dimensional slice sequences are segmented in the cross-sectional plane, and the segmentation results are combined to obtain the final results. Encoding, BDC- LSTM, and decoding include convolutional neural networks. The coding part uses asymmetric convolutional layers of different sizes and dilated convolutions to get multi-slice information and extend the convolutional layers' perceptual field. RESULTS This paper uses BDC-LSTM between the encoding and decoding parts of the algorithm. On the image segmentation of the brain in multiple cerebral infarcts dataset, accuracy rates of 0.876, 0.881, 0.887, and 0.912 were attained for the intersection of union (IOU), dice similarity coefficient (DS), sensitivity (SE), and predictive positivity value (PPV). The experimental findings demonstrate that the algorithm outperforms its rivals in accuracy. CONCLUSION This paper obtained segmentation results for three images using three models, ConvLSTM, Pyramid-LSTM, and BDC-LSTM, and compared them to verify that BDC-LSTM is the best method to perform the segmentation task for faster and more accurate detection of 3D medical images. We improve the convolutional neural network segmentation method to obtain medical images with high segmentation accuracy by solving the over-segmentation problem.
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Affiliation(s)
- Kelvin K L Wong
- School of Information and Electronics, Hunan City University, Yiyang 413000, China.
| | - Wanni Xu
- College of Technology and Engineering, National Taiwan Normal University, Taipei 106, Taiwan; Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
| | - Muhammad Ayoub
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - You-Lei Fu
- College of Technology and Engineering, National Taiwan Normal University, Taipei 106, Taiwan; Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China
| | - Huasen Xu
- Department of Civil Engineering, Shanghai Normal University, Shanghai 201418, China
| | - Ruizheng Shi
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Mu Zhang
- Department of Emergency, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Feng Su
- Department of Emergency, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhiguo Huang
- Department of Emergency, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Weimin Chen
- School of Information and Electronics, Hunan City University, Yiyang 413000, China
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Yuan L, Song J, Fan Y. FM-Unet: Biomedical image segmentation based on feedback mechanism Unet. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:12039-12055. [PMID: 37501431 DOI: 10.3934/mbe.2023535] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
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Affiliation(s)
- Lei Yuan
- The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China
| | - Jianhua Song
- The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China
- College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
| | - Yazhuo Fan
- College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
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Feng X, Wang T, Yang X, Zhang M, Guo W, Wang W. ConvWin-UNet: UNet-like hierarchical vision Transformer combined with convolution for medical image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:128-144. [PMID: 36650760 DOI: 10.3934/mbe.2023007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Convolutional Neural Network (CNN) plays a vital role in the development of computer vision applications. The depth neural network composed of U-shaped structures and jump connections is widely used in various medical image tasks. Recently, based on the self-attention mechanism, the Transformer structure has made great progress and tends to replace CNN, and it has great advantages in understanding global information. In this paper, the ConvWin Transformer structure is proposed, which refers to the W-MSA structure in Swin and combines with the convolution. It can not only accelerate the convergence speed, but also enrich the information exchange between patches and improve the understanding of local information. Then, it is integrated with UNet, a U-shaped architecture commonly used in medical image segmentation, to form a structure called ConvWin-UNet. Meanwhile, this paper improves the patch expanding layer to perform the upsampling operation. The experimental results on the Hubmap datasets and synapse multi-organ segmentation dataset indicate that the proposed ConvWin-UNet structure achieves excellent results. Partial code and models of this work are available at https://github.com/xmFeng-hdu/ConvWin-UNet.
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Affiliation(s)
- Xiaomeng Feng
- Department of Mathematics, School of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Taiping Wang
- Hangzhou Medipath Intelligent Technology Co., Ltd, Hangzhou, China
- School of Business, Macau University of Science and Technology, Macau, China
| | - Xiaohang Yang
- Hangzhou Medipath Intelligent Technology Co., Ltd, Hangzhou, China
| | - Minfei Zhang
- Hangzhou Medipath Intelligent Technology Co., Ltd, Hangzhou, China
| | - Wanpeng Guo
- Hangzhou Medipath Intelligent Technology Co., Ltd, Hangzhou, China
| | - Weina Wang
- Department of Mathematics, School of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China
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