MRAnnotator: A Multi-Anatomy Deep Learning Model for MRI Segmentation
Alexander Zhou Zelong Liu Andrew Tieu Nikhil Patel Sean Sun Anthony Yang Peter Choi Valentin Fauveau George Soultanidis Mingqian Huang Amish Doshi Zahi A. Fayad Timothy Deyer Xueyan Mei
Purpose To develop a deep learning model for multi-anatomy and many-class
segmentation of diverse anatomic structures on MRI imaging.
  Materials and Methods In this retrospective study, two datasets were curated
and annotated for model development and evaluation. An internal dataset of 1022
MRI sequences from various clinical sites within a health system and an
external dataset of 264 MRI sequences from an independent imaging center were
collected. In both datasets, 49 anatomic structures were annotated as the
ground truth. The internal dataset was divided into training, validation, and
test sets and used to train and evaluate an nnU-Net model. The external dataset
was used to evaluate nnU-Net model generalizability and performance in all
classes on independent imaging data. Dice scores were calculated to evaluate
model segmentation performance.
  Results The model achieved an average Dice score of 0.801 on the internal
test set, and an average score of 0.814 on the complete external dataset across
49 classes.
  Conclusion The developed model achieves robust and generalizable segmentation
of 49 anatomic structures on MRI imaging. A future direction is focused on the
incorporation of additional anatomic regions and structures into the datasets
and model.