专注分割非强化肿瘤区域，提升脑瘤自动分割精度
Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment
- 基于U-Net设计，重点识别MRI中的非强化肿瘤
- 非强化区与生存期及复发风险相关，需精准分割
- 适合临床肿瘤评估与放疗规划使用
本文提出一种基于U-Net的深度学习架构，用于在多种MRI模态下分割脑肿瘤，特别关注非强化肿瘤区域。尽管近年来如MICCAI挑战赛中已不再强调该区域，但研究表明其与患者生存时间及潜在肿瘤进展区域密切相关。因此，自动准确地界定非强化肿瘤的范围至关重要。该方法旨在提升对这一关键病理特征的识别能力，为临床诊断与治疗提供支持。
原文摘要 · Abstract (English)
A U-Net based deep learning architecture is designed to segment brain tumors as they appear on various MRI modalities. Special emphasis is lent to the non-enhancing tumor compartment. The latter has not been considered anymore in recent brain tumor segmentation challenges like the MICCAI challenges. However, it is considered to be indicative of the survival time of the patient as well as of areas of further tumor growth. Hence it deems essential to have means to automatically delineate its extension within the tumor.
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