Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy
  Reconstruction
Jianning Li Antonio Pepe Gijs Luijten Christina Schwarz-Gsaxner Jens Kleesiek Jan Egger
In this paper, we introduce a completion framework to reconstruct the
geometric shapes of various anatomies, including organs, vessels and muscles.
Our work targets a scenario where one or multiple anatomies are missing in the
imaging data due to surgical, pathological or traumatic factors, or simply
because these anatomies are not covered by image acquisition. Automatic
reconstruction of the missing anatomies benefits many applications, such as
organ 3D bio-printing, whole-body segmentation, animation realism,
paleoradiology and forensic imaging. We propose two paradigms based on a 3D
denoising auto-encoder (DAE) to solve the anatomy reconstruction problem: (i)
the DAE learns a many-to-one mapping between incomplete and complete instances;
(ii) the DAE learns directly a one-to-one residual mapping between the
incomplete instances and the target anatomies. We apply a loss aggregation
scheme that enables the DAE to learn the many-to-one mapping more effectively
and further enhances the learning of the residual mapping. On top of this, we
extend the DAE to a multiclass completor by assigning a unique label to each
anatomy involved. We evaluate our method using a CT dataset with whole-body
segmentations. Results show that our method produces reasonable anatomy
reconstructions given instances with different levels of incompleteness (i.e.,
one or multiple random anatomies are missing). Codes and pretrained models are
publicly available at https://github.com/Jianningli/medshapenet-feedback/
tree/main/anatomy-completor