DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark

TL;DR

DyABD introduces a challenging dynamic MRI dataset for abdominal muscle segmentation, highlighting the need for improved models and serving as a new benchmark for medical image segmentation progress.

Contribution

The paper presents the first abdominal muscle segmentation dataset from dynamic MRIs, including pre and post-operative images, and evaluates existing models' generalization capabilities.

Findings

Most models achieve a Dice Coefficient of 0.82 on DyABD.

Existing segmentation techniques have significant room for improvement.

DyABD serves as a new benchmark for medical image segmentation progress.

Abstract

This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annotations. DyABD is the first-of-its-kind in four key ways; (1) it proposes the first abdominal muscle segmentation task, (2) the dynamic MRIs are acquired whilst the patients perform various exercises, introducing extreme anatomical variability, making it one of the most challenging segmentation datasets to date, (3) it includes both pre and post corrective MRIs and (4) DyABD promotes clinical research into the high recurrence rates of abdominal hernias. Beyond dataset introduction, this work provides a comprehensive evaluation of the generalisation capabilities of existing segmentation models across Supervised, Few Shot and Zero Shot paradigms on the unseen DyABD dataset. This work reveals that there is still room for substantial improvement in the field of medical image segmentation, with the majority of techniques achieving a Dice Coefficient of 0.82. This work therefore sheds light on the true progress of the field and redefines the benchmark for progress in medical image segmentation.