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Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations

海绵状畸形 医学 疾病 磁共振成像 病理 放射科
作者
Ruige Zong,Tao Wang,Chunwang Li,Xinlin Zhang,Yuanbin Chen,Longxuan Zhao,Qixuan Li,Qinquan Gao,Dezhi Kang,Fuxin Lin,Tong Tong
出处
期刊:IEEE Transactions on Biomedical Engineering [Institute of Electrical and Electronics Engineers]
卷期号:: 1-12
标识
DOI:10.1109/tbme.2025.3539498
摘要

Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often numerous and intricate, making quantitative analysis of the lesions a labor-intensive task. Consequently, clinicians face challenges in quantitatively assessing the severity of lesions and determining whether lesions have progressed. To alleviate this problem, we propose a quantitative statistical framework for FCCM, which comprises an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module. Our framework demonstrates precise segmentation of the FCCM lesion based on efficient data annotation, achieving a Dice coefficient of 91.09%. More importantly, we focus on 3D quantitative statistics of lesions, which is combined with image registration to realize the quantitative comparison of lesions between different examinations of patients. A visualization framework has also been established for doctors to comprehensively compare and analyze lesions. The experimental results have demonstrated that our proposed framework not only obtains objective, accurate, and comprehensive quantitative statistical information, which provides a quantitative assessment method for disease progression and drug efficacy study, but also considerably reduces the manual measurement and statistical workload of lesions. This highlights the potential of practical application of the framework in FCCM clinical research and clinical decision-making.
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