SYSTEMATIC REVIEW article
Front. Med. Technol.
Sec. Cardiovascular Medtech
Volume 7 - 2025 | doi: 10.3389/fmedt.2025.1491197
This article is part of the Research TopicApplication of Deep Learning in Biomedical Image ProcessingView all 4 articles
Deep Learning for MRI-based Acute and Subacute Ischaemic Stroke Lesion Segmentation -A Systematic Review, Meta-Analysis, and Pilot Evaluation of Key Results
Provisionally accepted
- University of Edinburgh, Edinburgh, United Kingdom
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Segmentation of ischaemic stroke lesions from magnetic resonance images (MRI) remains a challenging task mainly due to the confounding appearance of these lesions with other pathologies, and variations in their presentation depending on the lesion stage (i.e., hyper-acute, acute, subacute and chronic). Works on the theme have been reviewed, but none of the reviews have addressed the seminal question on what would be the optimal architecture to address this challenge. We systematically reviewed the literature (2015-2023) for deep learning algorithms that segment acute and/or subacute stroke lesions on brain MRI seeking to address this question, meta-analysed the data extracted, and evaluated the results.Our review, registered in PROSPERO (ID: CRD42023481551), involved a systematic search from January 2015 to December 2023 in the following databases: IEE Explore, MEDLINE, ScienceDirect, Web of Science, PubMed, Springer, and OpenReview.net. We extracted sample characteristics, stroke stage, imaging protocols, and algorithms, and meta-analysed the data extracted. We assessed the risk of bias using NIH's study quality assessment tool, and finally, evaluated our results using data from the ISLES-2015-SISS dataset.From 1485 papers, 41 were ultimately retained. 13/41 studies incorporated attention mechanisms in their architecture, and 39/41 studies used the Dice Similarity Coefficient to assess algorithm performance. The generalisability of the algorithms reviewed was generally below par. In our pilot analysis, the UResNet50 configuration, which was developed based on the most comprehensive architectural components identified from the reviewed studies, demonstrated a better segmentation performance than the attention-based AG-UResNet50.We found no evidence that favours using attention mechanisms in deep learning architectures for acute stroke lesion segmentation on MRI data, and the use of a U-Net configuration with residual connections seems to be the most appropriate configuration for this task.
Keywords: AIS, acute ischemic stroke, AG, Attention Gate, BCE, Binary Cross-Entropy, BN, batch normalization, BOLD, blood oxygenation level dependent, CNN, Convolution Neural Network, CSF, cerebrospinal fluid, DenseNet, Dense Convolutional Network
Received: 04 Sep 2024; Accepted: 16 May 2025.
Copyright: © 2025 Baaklini and Valdés Hernández. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Maria Del Carmen Valdés Hernández, University of Edinburgh, Edinburgh, United Kingdom
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