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In this paper, we introduce a completion framework to reconstruct thegeometric shapes of various anatomies, including organs, vessels and muscles.Our work targets a scenario where one or multiple anatomies are missing in theimaging data due to surgical, pathological or traumatic factors, or simplybecause these anatomies are not covered by image acquisition. Automaticreconstruction of the missing anatomies benefits many applications, such asorgan 3D bio-printing, whole-body segmentation, animation realism,paleoradiology and forensic imaging. We propose two paradigms based on a 3Ddenoising 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 theincomplete instances and the target anatomies. We apply a loss aggregationscheme that enables the DAE to learn the many-to-one mapping more effectivelyand further enhances the learning of the residual mapping. On top of this, weextend the DAE to a multiclass completor by assigning a unique label to eachanatomy involved. We evaluate our method using a CT dataset with whole-bodysegmentations. Results show that our method produces reasonable anatomyreconstructions given instances with different levels of incompleteness (i.e.,one or multiple random anatomies are missing). Codes and pretrained models arepublicly available at https://github.com/Jianningli/medshapenet-feedback/tree/main/anatomy-completor

Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction