Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
Guiding LLM Decision-Making with Fairness Reward Models
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
Enhancing LLM Agent Safety via Causal Influence Prompting
토론
이 논문과 연결된 포럼 글이 아직 없습니다.