DyABD: the abdominal muscle segmentation in dynamic MRI benchmark.
Authors
Affiliations (9)
Affiliations (9)
- School of Medicine, University College Dublin, Dublin, Ireland. [email protected].
- Science Foundation Ireland Centre for Research Training in Machine Learning, Dublin, Ireland. [email protected].
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France.
- Aix Marseille Université, CNRS, CRMBM UMR, Marseille, 7339, France.
- School of Computer Science, University College Dublin, Dublin, Ireland.
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
- Département de Chirurgie, Hôpital Nord, APHM, Aix Marseille Université, Marseille, France.
- School of Medicine, University College Dublin, Dublin, Ireland.
- Science Foundation Ireland Centre for Research Training in Machine Learning, Dublin, Ireland.
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.