Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

TL;DR

This paper proposes a transfer learning approach with extensive data augmentation to improve cardiac MRI segmentation accuracy under respiratory motion artifacts, significantly enhancing baseline performance.

Contribution

It introduces a fine-tuning method using artifact-mimicking data augmentations to increase robustness of pretrained networks against respiratory motion artifacts.

Findings

Up to 0.06 Dice score improvement

4mm Hausdorff distance reduction

Enhanced segmentation robustness

Abstract

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).