MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision

Jianning Li, Gijs Luijten, Jan Egger

Abstract:

Objectives: The shape is commonly used to describe the
objects. State-of-the-art algorithms in medical imaging are
predominantly diverging from computer vision, where voxel
grids, meshes, point clouds, and implicit surface models are used.
This is seen from the growing popularity of ShapeNet (51,300
models) and Princeton ModelNet (127,915 models). However, a
large collection of anatomical shapes (e.g., bones, organs, vessels)
and 3D models of surgical instruments is missing.
Methods: We present MedShapeNet to translate data-
driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems.
As a unique feature, we directly model the majority of
shapes on the imaging data of real patients. We present use
cases in classifying brain tumors, skull reconstructions,
multi-class anatomy completion, education, and 3D printing.
Results: By now, MedShapeNet includes 23 datasets with
more than 100,000 shapes that are paired with annota-
tions (ground truth). Our data is freely accessible via a web
interface and a Python application programming inter-
face and can be used for discriminative, reconstructive,
and variational benchmarks as well as various applica-
tions in virtual, augmented, or mixed reality, and 3D
printing.
Conclusions: MedShapeNet contains medical shapes from
anatomy and surgical instruments and will continue to
collect data for benchmarks and applications. The project
page is: https://medshapenet.ikim.nrw/.
Keywords: 3D medical shapes; benchmark; anatomy edu-
cation; shapeomics; augmented reality; virtual reality