ArticleAJNR. American journal of neuroradiology2022
Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.
Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.Journal of applied clinical medical physics · 2025Article
- Artificial intelligence and stroke imaging.Current opinion in neurology · 2025Review
- Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging.Frontiers in neurology · 2025Article
- MR-CT image fusion method of intracranial tumors based on Res2Net.BMC medical imaging · 2024Article
- Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains.Epigenomes · 2022Article
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Authors and funding
15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
background and purposeSupervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans. MATERIALS AND
methodsTraining data consisted of 820 patients with baseline and follow-up NCCT from 3 Dutch acute ischemic stroke trials. A generative adversarial network was optimized to transform a follow-up scan with a lesion to a generated baseline scan without a lesion by generating a difference map that was subtracted from the follow-up scan. The generated difference map was used to automatically extract lesion segmentations. Segmentation of primary hemorrhagic lesions, hemorrhagic transformation of ischemic stroke, and 24-hour and 1-week follow-up infarct lesions were evaluated relative to expert annotations with the Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient.
resultsThe median Dice similarity coefficient was 0.31 (interquartile range, 0.08-0.59) and 0.59 (interquartile range, 0.29-0.74) for the 24-hour and 1-week infarct lesions, respectively. A much lower Dice similarity coefficient was measured for hemorrhagic transformation (median, 0.02; interquartile range, 0-0.14) and primary hemorrhage lesions (median, 0.08; interquartile range, 0.01-0.35). Predicted lesion volume and the intraclass correlation coefficient were good for the 24-hour (bias, 3 mL; limits of agreement, -64-59 mL; intraclass correlation coefficient, 0.83; 95% CI, 0.78-0.88) and excellent for the 1-week (bias, -4 m; limits of agreement,-66-58 mL; intraclass correlation coefficient, 0.90; 95% CI, 0.83-0.93) follow-up infarct lesions.
conclusionsAn unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.
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