Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation

2026-05-15

AI Summary

Standard CNNs for whole-heart multi-chamber CT segmentation often lack explicit anatomical constraints, compromising clinical reliability. This work proposes a lightweight approach that explicitly incorporates statistical shape priors through a shape-aware loss and a 3D U-Net variant guided by spatial label distribution heatmaps. The method is systematically evaluated on the MM-WHS CT and WHS++ datasets. Results reveal that, despite modern architectures implicitly learning substantial anatomical regularities from data, explicitly integrating handcrafted shape priors yields only marginal and inconsistent performance gains—and frequently leads to degradation. These findings underscore the limited added value of manually designed anatomical priors when deployed within highly data-driven deep learning models.

Abstract

Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on statistics derived from the training data, we evaluate whether lightweight explicit shape priors, implemented as shape-aware losses and spatial label distribution heatmap-guided U-Net variants, improve 3D cardiac segmentation on MM-WHS CT and WHS++. Across all experiments, a standard 3D U-Net surprisingly remained a very strong baseline, with handcrafted priors yielding at best marginal and inconsistent changes and often degrading performance. These results suggest that the baseline already captures substantial implicit anatomical regularities and that future gains will likely require more expressive learned priors rather than simple handcrafted anatomical shape constraints.

Problem

• cardiac segmentation
• anatomical shape priors
• deep learning
• CT imaging
• multi-compartment

Innovation

• shape priors
• cardiac segmentation
• U-Net
• anatomical plausibility
• deep learning