Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains

2026-07-06

AI Summary

This study addresses the performance degradation in cardiac structure segmentation from multi-source partially labeled echocardiograms, caused by domain shift and diverse missing label patterns. It presents the first systematic comparison of three loss functions—adaptive categorical cross-entropy (aCCE), Boundary Loss, and adaptive binary cross-entropy (aBCE)—across complex partial labeling scenarios, including single-domain versus cross-domain settings and single-label versus multi-label missingness. Experimental results demonstrate that Boundary Loss achieves the best performance in cross-domain tasks with multi-label missingness, while both aBCE and Boundary Loss significantly outperform other methods under cross-domain conditions with single-label missingness. All approaches exhibit robust performance in single-domain tasks. This work provides empirical evidence and practical guidance for selecting loss functions in partially labeled medical image segmentation.

Abstract

Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers. However, the development of effective automated segmentation models for multiple cardiac structures is challenged by the difficulty of training on datasets from different sources that are often partially-labelled. This study aims to address this challenge by evaluating the performance of three loss functions - adaptive categorical cross entropy (aCCE) loss, marginal loss, and the adaptive binary cross entropy (aBCE) loss - in handling partially-labelled data. We conduct a comprehensive comparison of these loss functions across multiple scenarios and network architectures: intra-domain and inter-domain tasks, with both single and multiple partial-labels, and varying proportions of fully-labelled to partially-labelled data. Our experiments reveal that all three loss functions exhibit strong performance in intra-domain segmentation tasks, effectively handling label variations within the same domain. For inter-domain tasks, where models are trained on datasets with a domain shift, the aBCE and marginal losses show superior performance when dealing with the case of one label being missing from some training examples. In scenarios involving more than one label being missing, marginal loss outperforms the other methods, demonstrating its robustness in such complex conditions. These results highlight the strengths of each loss function depending on the labelling scenario, emphasizing the importance of selecting the appropriate loss function to optimize model performance. This study represents the first investigation of techniques for handling partially-labelled data from multiple different domains in echocardiography segmentation and provides a comprehensive comparison of loss-based solutions.

Problem

• echocardiography segmentation
• partially labelled data
• multi-domain learning
• robust deep learning
• cardiac structure segmentation

Innovation

• partially-labelled data
• multi-domain segmentation
• loss function comparison
• echocardiography
• robust deep learning