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Ahmed R, Al Shehhi A, Hassan B, Werghi N, Seghier ML. An appraisal of the performance of AI tools for chronic stroke lesion segmentation. Comput Biol Med 2023; 164:107302. [PMID: 37572443 DOI: 10.1016/j.compbiomed.2023.107302] [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: 03/19/2023] [Revised: 07/18/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023]
Abstract
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data. Thanks to the availability of large datasets, the development of AI-based methods for lesion segmentation has immensely accelerated in the last decade. One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. This systematic review offers an appraisal of the impact of the ATLAS dataset in promoting the development of AI-based segmentation of stroke lesions. An examination of all published studies, that used the ATLAS dataset to both train and test their methods, highlighted an overall moderate performance (median Dice index = 59.40%) and a huge variability across studies in terms of data preprocessing, data augmentation, AI architecture, and the mode of operation (two-dimensional versus three-dimensional methods). Perhaps most importantly, almost all AI tools were borrowed from existing AI architectures in computer vision, as 90% of all selected studies relied on conventional convolutional neural network-based architectures. Overall, current research has not led to the development of robust AI architectures than can handle spatially heterogenous lesion patterns. This review also highlights the difficulty of gauging the performance of AI tools in the presence of uncertainties in the definition of the ground truth.
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Affiliation(s)
- Ramsha Ahmed
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Bilal Hassan
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Naoufel Werghi
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
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Li Y, Lao Q, Kang Q, Jiang Z, Du S, Zhang S, Li K. Self-supervised anomaly detection, staging and segmentation for retinal images. Med Image Anal 2023; 87:102805. [PMID: 37104995 DOI: 10.1016/j.media.2023.102805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/14/2022] [Accepted: 03/30/2023] [Indexed: 04/29/2023]
Abstract
Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.
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Affiliation(s)
- Yiyue Li
- Department of Ophthalmology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China; West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Qicheng Lao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China.
| | - Qingbo Kang
- West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Zekun Jiang
- West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Shiyi Du
- West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Shaoting Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Kang Li
- West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, China; Sichuan University Pittsburgh Institute, Chengdu, Sichuan, 610065, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China.
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Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal 2022; 79:102444. [DOI: 10.1016/j.media.2022.102444] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 03/09/2022] [Accepted: 04/01/2022] [Indexed: 02/07/2023]
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