Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures

TL;DR

This paper introduces Conformable Convolution, a new layer that explicitly enforces topological consistency in medical image segmentation by leveraging persistent homology to focus on critical structural features.

Contribution

The paper proposes Conformable Convolution and a Topological Posterior Generator that together improve topological preservation in deep learning models for complex anatomical structures.

Findings

Enhanced topological preservation in segmentation results

Improved accuracy on three diverse datasets

Effective integration with various architectures

Abstract

While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties. Despite their successes, deep learning models often struggle to accurately capture the connectivity and continuity of fine, sometimes pixel-thin, yet critical structures due to their reliance on implicit learning from data. Such shortcomings can significantly impact the reliability of analysis results and hinder clinical decision-making. To address this challenge, we introduce Conformable Convolution, a novel convolutional layer designed to explicitly enforce topological consistency. Conformable Convolution learns adaptive kernel offsets that preferentially focus on regions of high topological significance within an image. This prioritization is guided by our proposed Topological Posterior Generator (TPG) module, which leverages persistent homology. The TPG module identifies key topological features and guides the convolutional layers by applying persistent homology to feature maps transformed into cubical complexes. Our proposed modules are architecture-agnostic, enabling them to be integrated seamlessly into various architectures. We showcase the effectiveness of our framework in the segmentation task, where preserving the interconnectedness of structures is critical. Experimental results on three diverse datasets demonstrate that our framework effectively preserves the topology in the segmentation downstream task, both quantitatively and qualitatively.