  Abdominal organ segmentation from CT and MRI is an essential prerequisite for
surgical planning and computer-aided navigation systems. It is challenging due
to the high variability in the shape, size, and position of abdominal organs.
Three-dimensional numeric representations of abdominal shapes with point-wise
correspondence to a template are further important for quantitative and
statistical analyses thereof. Recently, template-based surface extraction
methods have shown promising advances for direct mesh reconstruction from
volumetric scans. However, the generalization of these deep learning-based
approaches to different organs and datasets, a crucial property for deployment
in clinical environments, has not yet been assessed. We close this gap and
employ template-based mesh reconstruction methods for joint liver, kidney,
pancreas, and spleen segmentation. Our experiments on manually annotated CT and
MRI data reveal limited generalization capabilities of previous methods to
organs of different geometry and weak performance on small datasets. We
alleviate these issues with a novel deep diffeomorphic mesh-deformation
architecture and an improved training scheme. The resulting method, UNetFlow,
generalizes well to all four organs and can be easily fine-tuned on new data.
Moreover, we propose a simple registration-based post-processing that aligns
voxel and mesh outputs to boost segmentation accuracy.
