CSF-Glioma: A Causal Segmentation Framework for Accurate Grading and Subregion Identification of Gliomas
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Published:2023-07-26
Issue:8
Volume:10
Page:887
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ISSN:2306-5354
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Container-title:Bioengineering
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language:en
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Short-container-title:Bioengineering
Author:
Zheng Yao1ORCID, Huang Dong12ORCID, Feng Yuefei1, Hao Xiaoshuo1, He Yutao1, Liu Yang12ORCID
Affiliation:
1. School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi’an 710032, China 2. Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, No. 169 Changle West Road, Xi’an 710032, China
Abstract
Deep networks have shown strong performance in glioma grading; however, interpreting their decisions remains challenging due to glioma heterogeneity. To address these challenges, the proposed solution is the Causal Segmentation Framework (CSF). This framework aims to accurately predict high- and low-grade gliomas while simultaneously highlighting key subregions. Our framework utilizes a shrinkage segmentation method to identify subregions containing essential decision information. Moreover, we introduce a glioma grading module that combines deep learning and traditional approaches for precise grading. Our proposed model achieves the best performance among all models, with an AUC of 96.14%, an F1 score of 93.74%, an accuracy of 91.04%, a sensitivity of 91.83%, and a specificity of 88.88%. Additionally, our model exhibits efficient resource utilization, completing predictions within 2.31s and occupying only 0.12 GB of memory during the test phase. Furthermore, our approach provides clear and specific visualizations of key subregions, surpassing other methods in terms of interpretability. In conclusion, the Causal Segmentation Framework (CSF) demonstrates its effectiveness at accurately predicting glioma grades and identifying key subregions. The inclusion of causality in the CSF model enhances the reliability and accuracy of preoperative decision-making for gliomas. The interpretable results provided by the CSF model can assist clinicians in their assessment and treatment planning.
Funder
National Natural Science Foundation of China Natural Science Foundation of Shaanxi Province Key Research and development plan of Shaanxi Province Natural Science Basic Research Program of Shaanxi Province
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