Automatic muscle segmentation on healthy abdominal MRI using nnUNet

Published: 27 Apr 2024, Last Modified: 03 Jun 2024MIDL 2024 Short PapersEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Medical imaging segmentation, Deep learning, Abdominal wall muscles
Abstract: Understanding the dynamics of the abdominal wall is essential in both physiology and surgery. To study the mechanical functionality of the abdominal wall, segmentation of the abdominal muscles could be useful but is a manual, tedious and time-consuming process. In this study, we assessed the capability of Deep Learning to automatically segment the abdominal muscles from the axial plane of a unique dynamic abdominal MRI (2D+t) dataset. The 2D slices were acquired while the subject performed various exercises. The State-of-the-Art segmentation model, nnUNet was trained on 5, 492 images from fifteen healthy subjects and tested it on 1, 801 images from five different subjects. We assessed the segmentation accuracy using DICE similarity coefficient, Hausdorff distance, as well as motion of the abdominal muscles. The ground truth and nnUNet segmentation showed high concordance, with a DICE over 0.87 for all exercises and muscles, and minimal differences in abdominal muscles motion. nnUNet effectively automates abdominal muscle segmentation, offering efficiency and new clinical applications in abdominal physiology.
Submission Number: 175