Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences
Year: 2026
Type: preprint
Abstract: 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 .
Source: medRxiv
Authors: Zhicheng Lu, Shahadat Uddin, Sergio Uribe, Sam White, Rodrigo Tomazini Martins, Shayne Chau, Abu Syed Md. Mosaddek, Md. Siddiqul Islam, Nusratun Nahar, AKM Azad, K. M. Nazmul Hossain, Habib Sadat Choudhury, K.M. Rakibul Hasan, Saad Aloteibi, Nabil Mosaddek, Samia Rahman, Md. Mostaque Hossain, K.M. Mehedi Hasan Sizar, Claudio Angione, Pietro Liò, Md Tauhidul Islam, Mohammad Ali Moni
Institutions: Charles Sturt University, Zhejiang University Robotics Institute, The University of Sydney, Monash University, SA Health, Mater Misericordiae Hospital, Government of New South Wales, Wagga Wagga Base Hospital, American International University-Bangladesh, Southeast University, Imam Mohammad ibn Saud Islamic University, Jahangirnagar University, King Saud University, Military Institute of Science and Technology, Teesside University, University of Cambridge, Stanford Medicine, Stanford University, The University of Queensland
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213255129