Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge

Area of Science:

• Cardiovascular Imaging
• Medical Image Analysis
• Machine Learning in Medicine

Background:

• Deep learning models are vital for cardiac MRI analysis, but accurate segmentation of the right ventricle remains difficult due to its complex geometry and borders.
• Existing methods struggle with pathologies like Dilated Right Ventricle and Tricuspid Regurgitation, necessitating improved segmentation techniques.

Purpose of the Study:

• To address the limitations in right ventricle segmentation, the M&Ms-2 challenge was organized to foster research in multi-disease, multi-view, and multi-center cardiac MRI.
• To evaluate automated segmentation algorithms on a diverse dataset encompassing various cardiac conditions and imaging parameters.

Main Methods:

• Collected 360 cardiac MRI (CMR) cases from three Spanish hospitals, featuring diverse pathologies and acquired using nine scanners from three vendors.
• Included both short-axis and long-axis 4-chamber views to capture comprehensive cardiac anatomy.

Main Results:

• The nnU-Net model demonstrated the best overall performance in right ventricle segmentation among challenge participants.
• Multi-view approaches provided additional valuable information, outperforming single-view methods in certain aspects.

Conclusions:

• Reliable automatic cardiac segmentation requires integrating data from multiple cardiac diseases, views, scanners, and acquisition protocols.
• Further research is needed to enhance the robustness and accuracy of right ventricle segmentation algorithms in complex clinical scenarios.