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, comprising 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
93.22\%. More importantly, we focus on quantitative statistics of lesions,
which is combined with image registration to realize the quantitative
comparison of lesions between different examinations of patients, and a
visualization framework has 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, assisting
clinical decision-making for FCCM and accelerating progress in FCCM clinical
research. This highlights the potential of practical application of the
framework in FCCM clinical research and clinical decision-making. The codes are
available at this https URL

建议了一个基于定量统计的模块化框架，用于定量评估遗传性脑海绵状血管瘤病变的严重程度和病变的进展，并且在临床决策过程中提供客观、准确、全面的定量统计信息，以加速脑海绵状血管瘤的临床研究进展和临床决策的制定。