Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917917023535104 |
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| author | Zong, Ruige Wang, Tao Li, Chunwang Zhang, Xinlin Chen, Yuanbin Zhao, Longxuan Li, Qixuan Gao, Qinquan Kang, Dezhi Lin, Fuxin Tong, Tong |
| author_facet | Zong, Ruige Wang, Tao Li, Chunwang Zhang, Xinlin Chen, Yuanbin Zhao, Longxuan Li, Qixuan Gao, Qinquan Kang, Dezhi Lin, Fuxin Tong, Tong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_15803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations Zong, Ruige Wang, Tao Li, Chunwang Zhang, Xinlin Chen, Yuanbin Zhao, Longxuan Li, Qixuan Gao, Qinquan Kang, Dezhi Lin, Fuxin Tong, Tong Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15803 |