Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models.

Area of Science:

• Medical Imaging Analysis
• Computational Anatomy
• Artificial Intelligence in Medicine

Background:

• Current methods for quantifying anatomical shape changes use global indexes insensitive to regional variations.
• Accurate assessment of pathology-driven anatomical remodeling is vital for diagnosing and treating various conditions.
• Deep learning models offer success in medical image analysis but often lack interpretability.

Purpose of the Study:

• To develop a novel, interpretable deep learning model for precise anatomical shape analysis.
• To enable visualization of classification spaces and anatomical variability for transparent decision-making.
• To accurately categorize healthy versus pathologically remodeled anatomical structures.

Main Methods:

• Utilized deep generative networks to model populations of anatomical segmentations via a hierarchy of conditional latent variables.
• Optimized a two-dimensional latent space at the highest hierarchy level for discriminating clinical conditions.
• Employed generative properties to visualize encoded anatomical variability within the segmentation space.

Main Results:

• Achieved high accuracy in categorizing healthy and remodeled left ventricles on multi-center and external validation datasets.
• Demonstrated high accuracy on hippocampal data from healthy controls and Alzheimer's disease patients (ADNI data).
• Enabled 3D visualization of global and regional anatomical features that effectively discriminate between conditions.

Conclusions:

• The proposed interpretable deep learning model accurately quantifies anatomical shape changes and discriminates between clinical conditions.
• The model provides transparent visualization of classification and anatomical variability, enhancing interpretability.
• This approach is scalable for high-throughput analysis in large-scale volumetric imaging studies.