Advancing cardiac MRI multi-structure segmentation: A semi-supervised multidimensional consistency constraint learning network

Abstract

Background

Deep convolutional neural networks (DCNNs) have been proposed for medical Magnetic Resonance Imaging (MRI) segmentation, but their effectiveness is often limited by challenges in semantic discrimination, boundary delineation, and spatial context modeling.

Purpose

To address these challenges, we present the Multidimensional Consistency Constraint Learning Network (MDCC-Net) for multi-structure segmentation of cardiac MRI using a semi-supervised approach.

Methods

MDCC-Net incorporates a shared encoder, multiple differentiated decoders, and leverages pyramid boundary consistency features and spatial consistency constraints. The model employs mutual consistency constraints and pseudo-labels to enhance segmentation performance. Additionally, MDCC-Net uses a combination of Dice loss and mean squared error loss to facilitate convergence and improve accuracy.

Results

Experiments on the ACDC cardiac MRI dataset demonstrate that MDCC-Net achieves state-of-the-art performance in multi-structure segmentation of the left ventricle (LV), myocardium (MYO), and right ventricle (RV). Specifically, MDCC-Net attained a Dice coefficient (Dice) of 0.8763 and a Jaccard index of 0.7906 on average. The right ventricle's Average Surface Distance (ASD) reached a best performance of 0.5391, and the left ventricle's Dice attained an optimal value of 0.8965. These results highlight the model's superior ability to utilize semi-supervised data through consistency and entropy minimization constraints. In addition, the generalization of MDCC-Net is verified on the M&Ms dataset.

Conclusions

MDCC-Net significantly enhances the multi-structure segmentation of cardiac MRI under multidimensional consistency constraints. This approach provides a foundational study for integrating multifeature fusion in clinical automated and semiautomated multi-organ and multi-tissue segmentation, thus potentially improving diagnostic and treatment planning processes in clinical settings.