Clinical Inspired MRI Lesion Segmentation
Journal:
arXiv
Published Date:
Feb 22, 2025
Abstract
Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting
pathological tissues in various diseases. Different MRI sequences have
different contrast mechanisms and sensitivities for different types of lesions,
which pose challenges to accurate and consistent lesion segmentation. In
clinical practice, radiologists commonly use the sub-sequence feature, i.e. the
difference between post contrast-enhanced T1-weighted (post) and
pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we
propose a residual fusion method to learn subsequence representation for MRI
lesion segmentation. Specifically, we iteratively and adaptively fuse features
from pre- and post-contrast sequences at multiple resolutions, using dynamic
weights to achieve optimal fusion and address diverse lesion enhancement
patterns. Our method achieves state-of-the-art performances on BraTS2023
dataset for brain tumor segmentation and our in-house breast MRI dataset for
breast lesion segmentation. Our method is clinically inspired and has the
potential to facilitate lesion segmentation in various applications.
