Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

TL;DR

This paper introduces a multi-sequence deep learning approach that uses synthetic images generated via GANs to enhance the segmentation accuracy of scar tissue in LGE-MRI cardiac images, outperforming traditional methods.

Contribution

It presents a novel method combining GAN-based modality translation and multi-sequence training to improve cardiac MRI segmentation accuracy.

Findings

Multi-sequence training with synthetic images improves segmentation accuracy.

Synthetic images enhance model robustness across different MRI sequences.

The approach outperforms conventional single-sequence segmentation methods.

Abstract

Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.