Ensembling Transformer-Based Models for 3D Ischemic Stroke Segmentation in Non-Contrast CT
Cherikbayeva L. Berikov V. Melis Z. Yeleussinov A. Baigozhanova D. Tasbolatuly N. Temirbekova Z. Mikhailapov D.
September 2025Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2025#15Issue 17
Ischemic stroke remains one of the leading causes of mortality and disability, and accurate segmentation of the affected areas on CT brain images plays a crucial role in timely diagnosis and clinical decision-making. This study proposes an ensemble approach based on the combination of the transformer-based models SE-UNETR and Swin UNETR using a weighted voting strategy. Its performance was evaluated using the Dice similarity coefficient, which quantifies the overlap between the predicted lesion regions and the ground-truth annotations. In this study, three-dimensional CT scans of the brain from 98 patients with a confirmed diagnosis of acute ischemic stroke were used. The data were provided by the International Tomography Center, SB RAS. The experimental results demonstrated that the ensemble based on transformer models significantly outperforms each individual model, providing more stable and accurate predictions. The final Dice coefficient reached 0.7983, indicating the high effectiveness of the proposed approach for ischemic lesion segmentation in CT images. The analysis showed more precise delineation of ischemic lesion boundaries and a reduction in segmentation errors. The proposed method can serve as an effective tool in automated stroke diagnosis systems and other applications requiring high-accuracy medical image analysis.
CT , deep learning , ensemble of models , ischemic stroke , segmentation , Swin Transformer , UNETR
Text of the article Перейти на текст статьи
Department of Computer Science, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Mechanics and Mathematics, Novosibirsk State University, Novosibirsk, 630090, Russian Federation
Sobolev Institute of Mathematics SB RAS, Novosibirsk, 630090, Russian Federation
Higher School of Information Technology and Engineering, Astana International University, Astana, 010000, Kazakhstan
Department of Computer Engineering, Astana IT University, Astana, 010000, Kazakhstan
Department of Computer Science
Department of Mechanics and Mathematics
Sobolev Institute of Mathematics SB RAS
Higher School of Information Technology and Engineering
Department of Computer Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026