Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Accurate automatic segmentation of gliomas and intra-tumoral structures is crucial for brain tumor treatment planning and follow-up.
• Deep Neural Networks (DNNs) are widely used for brain tumor segmentation from MRI, but often lack transparency, hindering clinical integration.
• Interpretability of DNNs is essential for building trust and facilitating their adoption in medical practice.

Purpose of the Study:

• To explore techniques for explaining the functional organization of brain tumor segmentation models.
• To extract visualizations of internal concepts to understand how DNNs achieve accurate tumor segmentations.
• To analyze similarities and differences in segmentation processes across different network architectures.

Main Methods:

• Trained three distinct DNNs using standard architectures on the BraTS 2018 dataset for brain tumor segmentation.
• Employed visualization techniques to examine internal feature maps and filter-level concepts.
• Assessed model uncertainty to provide qualitative evidence for predictions.

Main Results:

• Demonstrated that brain tumor segmentation networks learn human-understandable, disentangled concepts at the filter level.
• Revealed that networks adopt a top-down or hierarchical approach for localizing tumor components.
• Extracted visualizations of internal feature maps and provided uncertainty measures for model predictions.

Conclusions:

• The study highlights that DNNs for brain tumor segmentation exhibit understandable organizational principles.
• Visualizing internal concepts and providing uncertainty measures can enhance the interpretability of these models.
• Increased transparency and understandability of DNNs may facilitate their acceptance and integration into clinical neuro-oncology practice.