acedeep learning-based ischemic stroke lesion segmentation for neuroimaging workflows
Source article: Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions
Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. Deep learning (DL) aims to accelerate and standardize lesion delineation to augment neuroimaging workflows. We conducted a narrative review of DL-based ischemic stroke lesion segmentation studies published from 2020 to 2025. PubMed, Google Scholar, Scopus, and IEEE Xplore were searched; ~ 500 records were identified, and 40 full-text studies were included after scr…
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"Brain CT Scan for Stroke Diagnosis" by Goleisureintl is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
By July 2026, a narrative review of 40 studies from 2020-2025 found deep learning, led by U-Net variants with residual and attention mechanisms and standardized pipelines like nnU-Net, increasingly achieved high Dice scores on MRI DWI/ADC, with many reports above 0.80 and recent transformer and ensemble multisite models approaching 0.90, while CT performance was lower and more variable.
The findings matter because faster, standardized lesion delineation could improve time-sensitive stroke treatment selection and prognostication, but the evidence base as of the review date was largely retrospective and may not translate to routine care, especially for CT and under-resourced settings, leaving prospective multicenter validation and equitable deployment as unresolved steps.
- Narrative review of 40 studies from 2020-2025 identified from ~500 records across PubMed, Google Scholar, Scopus, and IEEE Xplore.
- U-Net backbones and variants remained dominant, with gains from residual and attention mechanisms and standardized pipelines like nnU-Net.
- MRI performance was highest: many DWI/ADC studies reported DSC > 0.80, residual/attention U-Nets ~0.87, nnU-Net ~0.80-0.82, and since 2023 multisite transformer/ensemble reports approaching ~0.90.
- CT/NCCT segmentation was more variable at DSC ~0.35-0.65, though late-period hybrid CNN-transformer and multimodal approaches reported approaching ~0.8.
Deep learning models, especially U-Net variants and newer transformer ensembles, improved ischemic stroke lesion segmentation on MRI, achieving Dice scores above 0.80 and approaching 0.90 in multisite DWI reports, to support faster treatment selection and quantification.
Most studies were retrospective and may not reflect real-world performance and access constraints, and CT segmentation remains constrained by subtle early ischemic changes and poor generalization, limiting equitable clinical deployment.
The rundown
The review synthesized 40 full-text studies after screening ~500 records from 2020-2025, extracting modality, architecture, and Dice similarity coefficient. U-Net variants dominated, with exploratory LOESS curves used only to visualize temporal trends, not for inference. Trends suggested gradual improvement for MRI and a U-shaped trajectory for CT.
While MRI results were most convincing, CT/NCCT remained more variable due to subtle early ischemic change. The authors concluded DL segmentation is maturing toward clinical viability for MRI but requires prospective, multicenter validation and scalable deployment pathways to enable equitable impact and reflect real-world performance.
Most included studies were retrospective and may not reflect real-world performance, access constraints, and generalization, particularly for CT where subtle early ischemic changes limit reliability.
Sources
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-07-17
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