Factorised spatial representation learning: application in semi-supervised myocardial segmentation

TL;DR

This paper introduces a novel latent space factorisation method based on cycle-consistency for medical image analysis, enabling semi-supervised myocardial segmentation by disentangling anatomical and imaging features.

Contribution

It proposes a new factorisation approach in latent space using cycle-consistency, specifically applied to cardiac MRI segmentation, improving semi-supervised learning performance.

Findings

Achieves comparable results to fully supervised models with fewer labelled images.

Demonstrates effective disentanglement of anatomical and imaging features.

Validates method on ACDC and Edinburgh QMRI datasets.

Abstract

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method's utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.