TextBraTS: Bridging 3D MRI, Text, and Genotype for Data-Efficient Glioma Segmentation and Analysis.
TL;DR
This work quantifies the "Semantic Gain" of structured textual priors as a proof-of-concept, providing a controlled benchmark for the potential of structured text guidance and providing a robust foundation for integrated radiogenomic analysis and computer-aided diagnosis research.
Abstract
Precise delineation of lesion boundaries is a cornerstone of radiological image analysis, directly impacting downstream prognostic accuracy. However, manual annotation of 3D volumetric data is labor-intensive and expert-dependent, creating a significant bottleneck in clinical workflows. While multimodal text-guided segmentation has gained traction, glioma research remains constrained by data scarcity and strict ethical regulations, resulting in a lack of integrated datasets and domain-specific methodologies. To bridge this gap, we introduce TextBraTS, a high-quality, open-access dataset featuring aligned 3D MRI, expert-verified textual descriptions, and genotype information. We establish a Data-Centric Paradigm encompassing an LLM-driven Clinical Narrative Standardization Protocol that distills unstructured reports into structured semantic priors. Furthermore, we propose a Multi-level Gated Fusion (MGF) network to dynamically integrate these expert-curated semantic attributes into 3D vision backbones. Benchmarked against state-of-the-art methods, our framework demonstrates superior precision in both glioma segmentation and molecular subtyping. This work quantifies the "Semantic Gain" of structured textual priors as a proof-of-concept, providing a controlled benchmark for the potential of structured text guidance and providing a robust foundation for integrated radiogenomic analysis and computer-aided diagnosis research.