Deep learning for multimodal brain tumor segmentation: Architectures, fusion, robust learning, and deployment...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Accurate brain tumor segmentation from multimodal MRI is crucial for clinical applications.
• Deep learning models have advanced segmentation but face challenges in reliability.
• Vulnerabilities include missing data, cross-center shifts, and subregion-specific failures.

Purpose of the Study:

• Critically review deep learning for multimodal brain tumor segmentation from a deployment-oriented, failure-focused perspective.
• Introduce a unifying framework linking input reliability, fusion-architecture co-design, failure mechanisms, and clinical triage.
• Compare architectural paradigms for robustness and reliability under realistic conditions.

Main Methods:

• Developed a unifying framework for analyzing deep learning segmentation failures.
• Reinterpreted multimodal fusion as a reliability-allocation problem.
• Synthesized robust learning approaches and assessed risk control mechanisms like interpretability and uncertainty estimation.

Main Results:

• Identified persistent instability in enhancing tumor (ET) and tumor core (TC) segmentation.
• Highlighted issues with benchmark performance saturation and inconsistent reporting.
• Found insufficient stress testing, center-stratified evaluation, and computational transparency.

Conclusions:

• Clinical reliability, not just benchmark accuracy, is paramount for deep learning in brain tumor segmentation.
• Future progress requires addressing subregion-level reliability under realistic deployment conditions.
• Emphasis needed on robust learning, uncertainty quantification, and rigorous evaluation.