Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
470-02-080 ISMRM Abstract

Deep Learning and Brain Parcellation–Based Machine Learning Model for Prognosis after Acute Ischemic Stroke

Primary: Diffusion - DWI/DTI/DKI
Secondary: Neuro - Stroke and Cerebrovascular Disorders
470-02-080 · Decoding Stroke with Imaging and AI: New Frontiers in Cerebrovascular Research · Tuesday, 12 May, 8:20 AM–9:15 AM · Traditional Posters | Exhibition Hall
Keywords: Stroke Deep Learning Segmentation Machine learning classification Modified Rankin Scale Brain parcellation
Accepted
Tung-Yang Lee1,2, Chun-Jung Juan 3,4,5,6, Shao-Chieh Lin7, Chia-Ching Chang3,8, Cheng-Hsuan Juan5,9,10, Cheng-En Juan1,11, Ya-Hui Li9,12, Yi-Jui Liu13
1Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan
2Post Graduate Year, Show Chwan Memorial Hospital, Changhua, Taiwan
3Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Hsinchu City, Taiwan
4Department of Radiology, China Medical University, Taichung, Taiwan
5Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
6Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
7Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan
8Department of Radiology, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Hsinchu City, Taiwan
9Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
10Department of Medical Imaging, National Taiwan University, Taipei, Taiwan
11Post Graduate Year, China Medical University Hospital, Taichung, Taiwan
12Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan
13Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
Presenting Author: Chun-Jung Juan

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References

1. The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net. Li YH, Lin SC, Chung HW, Chang CC, Peng HH, Huang TY, Shen WC, Tsai CH, Lo YC, Lee TY, Juan CH, Juan CE, Chang HC, Liu YJ, Juan CJ. Eur Radiol. 2023 Sep;33(9):6157-6167. doi: 10.1007/s00330-023-09622-z. Epub 2023 Apr 25. PMID: 37095361 [doi] [pmid]
2. mproving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds. Juan CJ, Lin SC, Li YH, Chang CC, Jeng YH, Peng HH, Huang TY, Chung HW, Shen WC, Tsai CH, Chang RF, Liu YJ. Eur Radiol. 2022 Aug;32(8):5371-5381. doi: 10.1007/s00330-022-08633-6. Epub 2022 Feb 24. PMID: 35201408 [doi] [pmid]
3. Borsos B, Allaart CG, van Halteren A. Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data. Artif Intell Med. 2024;147:102719. doi: 10.1016/j.artmed.2023.102719 [doi]
4. Ramos LA, van Os H, Hilbert A, Olabarriaga SD, van der Lugt A, Roos Y, van Zwam WH, van Walderveen MAA, Ernst M, Zwinderman AH, et al. Combination of Radiological and Clinical Baseline Data for Outcome Prediction of Patients With an Acute Ischemic Stroke. Front Neurol. 2022;13:809343. doi: 10.3389/fneur.2022.809343 [doi]
5. Xie Y, Jiang B, Gong E, Li Y, Zhu G, Michel P, Wintermark M, Zaharchuk G. JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information. AJR Am J Roentgenol. 2019;212:44-51. doi: 10.2214/AJR.18.20260 [doi]
6. Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, Schonenberger S, Heiland S, Ulfert C, Ringleb PA, et al. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke. 2020;51:3541-3551. doi: 10.1161/STROKEAHA.120.030287 [doi]

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http://echo.ismrm.org/p/ISMRM2026/470-02-080