DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
For medical image segmentation researchers, this dataset and evaluation reveal the limitations of current methods on a challenging, clinically relevant task, redefining the benchmark for progress.
This work introduces DyABD, the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs from hernia patients, and evaluates existing segmentation models, finding that most achieve a Dice coefficient of 0.82, indicating substantial room for improvement.
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.