Work

Optimizing Cardiac MRI Segmentation: An Ensemble Approach with U-Net Variants

Year: 2025

Type: conference-paper

Abstract: Segmentation of cardiac magnetic resonance images is a critical task in medical imaging, particularly to delineate the left and right ventricles and the myocardium. This study aims to improve segmentation performance using an ensemble approach with variants of the U-Net architecture, a widely adopted deep learning model for image segmentation. Multiple segmentation models were trained and optimized, and their outputs were combined using threshold-based binary conversion. Two ensemble strategies were evaluated: (1) Averaging, where the mean value of the binary masks at each pixel location was calculated to smooth discrepancies among model predictions, and (2) Voting, where majority voting determined the final pixel classification. The proposed ensemble approach demonstrates robustness to individual model errors and improves segmentation consistency.

DOI: 10.3311/wins2025-012

Authors: Botond Bárdos-Deák, Adrián Tibor Bodai, Mohammed Salah Al-Radhi

Institution: Budapest University of Technology and Economics

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Funders: European Commission, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, National Research, Development and Innovation Office

Award: ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI