Interpretable Semantic Medical Image Segmentation With Style and Confidence

Area of Science:

• Medical Imaging
• Deep Learning
• Computer Vision

Background:

• Deep learning for medical image segmentation faces challenges due to limited labeled data and model interpretability.
• The
• black-box
• nature of deep learning models hinders clinical deployment.

Purpose of the Study:

• To introduce Generative Adaptable Segmentation Evolution (GASE), a novel framework for robust and interpretable medical image segmentation.
• To address extreme data scarcity and improve model adaptability to unseen image variations.

Main Methods:

• GASE employs an end-to-end, style-based generative adversarial network (GAN) framework.
• A style-learning generator captures input style features and diversifies training data through style interpolation.
• A segmentation-based discriminator estimates mask reliability and improves adaptation to unseen styles.

Main Results:

• GASE demonstrates high segmentation accuracy for multiple tissues in knee and pelvis MR images.
• The framework exhibits strong adaptability to unseen acquisition-level image variations (e.g., protocols, sequences, demographics).
• GASE provides intrinsic interpretability through input validity assessment and output reliability estimation.

Conclusions:

• GASE offers a robust solution for medical image segmentation under extreme data scarcity.
• The framework's adaptability and interpretability show significant potential for reliable clinical deployment in medical imaging.