Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

TL;DR

This paper introduces a deep learning framework that effectively removes motion artifacts from MRI scans, enabling more accurate brain tissue segmentation even in challenging motion-affected images.

Contribution

A novel disentanglement learning network that jointly performs motion correction and tissue segmentation, improving robustness to motion artifacts in MRI.

Findings

Outperforms state-of-the-art methods on pediatric motion MRI data

Generates motion-corrected images, deformation maps, and segmentation masks

Demonstrates superior accuracy in artifact-affected MRI segmentation

Abstract

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motioncorrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans.