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

TL;DR

This work tackles the need for objective, scalable quantification of familial cerebral cavernous malformations (FCCM) by introducing a three-component framework: efficient annotation, FCCM lesion segmentation, and lesion quantitative statistics (QS) coupled with image registration for cross-visit comparison. It combines SAM-based and BBTS-based annotation with iterative training to produce high-quality lesion masks, achieving a Dice coefficient of $93.22\%$ on segmentation. The QS module computes lesion counts and volumes and uses an Intersection over Boundary Cube (IoC) measure to match lesions across examinations, providing detailed progression insights and visualization to clinicians. The approach demonstrates substantial efficiency gains in annotation, high segmentation accuracy, and practical tools for disease progression assessment and drug-efficacy studies in FCCM, with code available at the referenced GitHub repository. Overall, the framework offers a scalable, objective pathway toward routine quantitative FCCM analysis in clinical research and decision-making.

Abstract

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 https://github.com/6zrg/Quantitative-Statistics-of-FCCM.

Figures (11)

• Figure 1: Complex FCCM lesion cases. The green markings represent the regions of lesions.
• Figure 1: FCCM quantitative statistical volume distribution.
• Figure 2: FCCM quantitative statistical framework. The visualization of volume distribution (see Figure \ref{['9']} in Appendix A for details) displays the indexes and volume information of lesions. The "grow" and "shrink" categories respectively represent the volume pairs of lesions with increased or decreased volume between two inspections. The "emerge" and "vanish" categories depict the lesions that are newly developed or disappeared (or unsuccessfully matched) compared to the previous inspection.
• Figure 2: Previous inspection visualization collage.
• Figure 3: Efficient annotation module.