Towards Automatic Abdominal MRI Organ Segmentation: Leveraging
  Synthesized Data Generated From CT Labels
Cosmin Ciausu Deepa Krishnaswamy Benjamin Billot Steve Pieper Ron Kikinis Andrey Fedorov
Deep learning has shown great promise in the ability to automatically
annotate organs in magnetic resonance imaging (MRI) scans, for example, of the
brain. However, despite advancements in the field, the ability to accurately
segment abdominal organs remains difficult across MR. In part, this may be
explained by the much greater variability in image appearance and severely
limited availability of training labels. The inherent nature of computed
tomography (CT) scans makes it easier to annotate, resulting in a larger
availability of expert annotations for the latter. We leverage a
modality-agnostic domain randomization approach, utilizing CT label maps to
generate synthetic images on-the-fly during training, further used to train a
U-Net segmentation network for abdominal organs segmentation. Our approach
shows comparable results compared to fully-supervised segmentation methods
trained on MR data. Our method results in Dice scores of 0.90 (0.08) and 0.91
(0.08) for the right and left kidney respectively, compared to a pretrained
nnU-Net model yielding 0.87 (0.20) and 0.91 (0.03). We will make our code
publicly available.