  Background: Segmentation of organs and structures in abdominal MRI is useful
for many clinical applications, such as disease diagnosis and radiotherapy.
Current approaches have focused on delineating a limited set of abdominal
structures (13 types). To date, there is no publicly available abdominal MRI
dataset with voxel-level annotations of multiple organs and structures.
Consequently, a segmentation tool for multi-structure segmentation is also
unavailable. Methods: We curated a T1-weighted abdominal MRI dataset consisting
of 195 patients who underwent imaging at National Institutes of Health (NIH)
Clinical Center. The dataset comprises of axial pre-contrast T1, arterial,
venous, and delayed phases for each patient, thereby amounting to a total of
780 series (69,248 2D slices). Each series contains voxel-level annotations of
62 abdominal organs and structures. A 3D nnUNet model, dubbed as
MRISegmentator-Abdomen (MRISegmentator in short), was trained on this dataset,
and evaluation was conducted on an internal test set and two large external
datasets: AMOS22 and Duke Liver. The predicted segmentations were compared
against the ground-truth using the Dice Similarity Coefficient (DSC) and
Normalized Surface Distance (NSD). Findings: MRISegmentator achieved an average
DSC of 0.861$\pm$0.170 and a NSD of 0.924$\pm$0.163 in the internal test set.
On the AMOS22 dataset, MRISegmentator attained an average DSC of
0.829$\pm$0.133 and a NSD of 0.908$\pm$0.067. For the Duke Liver dataset, an
average DSC of 0.933$\pm$0.015 and a NSD of 0.929$\pm$0.021 was obtained.
Interpretation: The proposed MRISegmentator provides automatic, accurate, and
robust segmentations of 62 organs and structures in T1-weighted abdominal MRI
sequences. The tool has the potential to accelerate research on various
clinical topics, such as abnormality detection, radiotherapy, disease
classification among others.
