Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR...

Area of Science:

• Medical imaging analysis
• Artificial intelligence in cardiology
• Biomedical image segmentation

Background:

• Myocardial pathology segmentation (MyoPS) is crucial for assessing myocardial infarction (MI) severity.
• Deep learning (DL) methods show promise but struggle with generalizability due to domain shifts in cardiac magnetic resonance (CMR) imaging.
• Limited generalizability hinders accurate quantification of myocardial scar and edema on unseen data.

Purpose of the Study:

• To propose a novel hierarchical semantic concept segmentation framework (HSCM-Net) to enhance the generalizability of MyoPS.
• To address cross-domain distribution shifts in multisequence CMR images.
• To improve the learning of domain-invariant pathology information for more robust segmentation.

Main Methods:

• HSCM-Net decomposes CMR images into independent concept variables (shape, pathology, appearance) with hierarchical priors.
• It models anatomy and pathology segmentation as global and local concepts, respectively.
• A variational inference (VI) framework implemented with deep neural networks approximates conceptual variable posteriors.

Main Results:

• HSCM-Net demonstrated advantageous generalizability on three-domain multisequence CMR datasets.
• Achieved a MyoPS Dice score of 0.577 on unseen target domains, outperforming existing methods.
• The framework effectively integrates complementary information from multisequence CMR images.

Conclusions:

• HSCM-Net significantly enhances the generalizability of myocardial pathology segmentation across different CMR domains.
• The proposed hierarchical semantic concept modeling approach is effective in learning domain-invariant features.
• This framework offers a promising solution for accurate and reliable assessment of myocardial infarction severity.