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

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.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Biomedical Engineering
Published
2025-02-06
DOI
https://doi.org/10.1109/tbme.2025.3539498
Primary Topic
Vascular Malformations Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Dezhi Kang, Chunwang Li, Yuanbin Chen, Fuxin Lin et al.
IEEE Transactions on Biomedical Engineering
Vascular Malformations Diagnosis and Treatment
article

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

Dezhi Kang, Chunwang Li, Yuanbin Chen, Fuxin Lin, Longxuan Zhao, Qinquan Gao, Ruige Zong, Xinlin Zhang, Tao Wang, Tong Tong, Qixuan Li
article en

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, 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.

IEEE Transactions on Biomedical EngineeringVol. 72(7)
Fujian Medical University (CN), Fuzhou University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 96%
Vascular Malformations Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.