Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences
- 1University of Queensland
Abstract
Stroke remains one of the leading causes of disability and mortality worldwide, where timely and accurate diagnosis is critical for guiding treatment and improving patient outcomes. However, a global shortage of trained clinicians and radiologists continues to limit rapid and reliable interpretation of neuroimaging, particularly in resource-constrained settings. Artificial intelligence (AI) has emerged as a promising solution to this challenge by enabling efficient analysis of medical images. Here we present an Integrated Stroke Diagnosis System for MRI (ISDS-MRI), a unified framework that leverages graph neural networks and sequence-specific feature modeling for comprehensive ischemic stroke analysis. This framework is designed to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI data, while accommodating incomplete combinations of MRI sequences. To ensure generalizability, we evaluate our approach across multiple publicly available MRI datasets and introduce a newly curated dataset, BGD-MRIS, comprising 532 MRI scans from three hospitals in Bangladesh. This newly curated dataset provides a multi-center MRI cohort from a resource-constrained setting, offering an additional test bed for evaluating stroke AI across heterogeneous clinical imaging protocols. Experimental results demonstrate that ISDS-MRI achieves a Dice score of 0.725 for lesion segmentation, a AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice score and 2.6% in detection performance, while reducing volume estimation relative error by 1.9%. These results highlight the robustness and clinical potential of ISDS-MRI for scalable and comprehensive stroke diagnosis from MRI. The BGD-MRIS dataset will be publicly available at https://github.com/Zhicheng-Lu/stroke_mri.
Related
- Multi-scale Gaussian product-of-experts for missing sequence multiple sclerosis lesion segmentation
- 2D U‐Net Framework for Multiclass Automated Segmentation of Healthy Structures, Ischemic Stroke, and Glioblastoma in the Rat Brain
- Automated corneal endothelial cell detection and segmentation in specular microscopy images using Attention and Faster U-Net
- Diagnostic accuracy, time window estimation, and prognostic value of diffusion-weighted MRI in acute ischemic stroke: a systematic review and meta-analysis
- Detection of high-risk sequence type ST131 among multidrug-resistant Escherichia coli causing urinary tract infections in Cusco, Peru
BibTeX
@misc{lu2026ischemic,
title = {{Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences}},
author = {Z. Lu and S. Uddin and S. Uribe and S. White and R. T. Martins and S. Chau and A. S. M. Mosaddek and M. S. Islam and N. Nahar and A. K. M. Azad and K. M. N. Hossain and H. S. Choudhury and K. M. R. Hasan and N. Mosaddek and S. Rahman and M. M. Hossain and K. M. M. H. Sizar and C. Angione and P. Lio and M. T. Islam and M. A. Moni},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.12.26362795},
url = {https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences
AU - Z. Lu
AU - S. Uddin
AU - S. Uribe
AU - S. White
AU - R. T. Martins
AU - S. Chau
AU - A. S. M. Mosaddek
AU - M. S. Islam
AU - N. Nahar
AU - A. K. M. Azad
AU - K. M. N. Hossain
AU - H. S. Choudhury
AU - K. M. R. Hasan
AU - N. Mosaddek
AU - S. Rahman
AU - M. M. Hossain
AU - K. M. M. H. Sizar
AU - C. Angione
AU - P. Lio
AU - M. T. Islam
AU - M. A. Moni
PY - 2026
DA - 2026/09/12
PB - medRxiv
DO - 10.64898/2026.09.12.26362795
UR - https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1
AB - Stroke remains one of the leading causes of disability and mortality worldwide, where timely and accurate diagnosis is critical for guiding treatment and improving patient outcomes. However, a global shortage of trained clinicians and radiologists continues to limit rapid and reliable interpretation of neuroimaging, particularly in resource-constrained settings. Artificial intelligence (AI) has emerged as a promising solution to this challenge by enabling efficient analysis of medical images. Here we present an Integrated Stroke Diagnosis System for MRI (ISDS-MRI), a unified framework that leverages graph neural networks and sequence-specific feature modeling for comprehensive ischemic stroke analysis. This framework is designed to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI data, while accommodating incomplete combinations of MRI sequences. To ensure generalizability, we evaluate our approach across multiple publicly available MRI datasets and introduce a newly curated dataset, BGD-MRIS, comprising 532 MRI scans from three hospitals in Bangladesh. This newly curated dataset provides a multi-center MRI cohort from a resource-constrained setting, offering an additional test bed for evaluating stroke AI across heterogeneous clinical imaging protocols. Experimental results demonstrate that ISDS-MRI achieves a Dice score of 0.725 for lesion segmentation, a AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice score and 2.6% in detection performance, while reducing volume estimation relative error by 1.9%. These results highlight the robustness and clinical potential of ISDS-MRI for scalable and comprehensive stroke diagnosis from MRI. The BGD-MRIS dataset will be publicly available at https://github.com/Zhicheng-Lu/stroke_mri.
ER -