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research article

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

Li, Jianning
•
Zhou, Zongwei
•
Yang, Jiancheng  
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2024
Biomedizinische Technik

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. 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. By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications.

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Type
research article
DOI
10.1515/bmt-2024-0396
Scopus ID

2-s2.0-85214373656

PubMed ID

39733351

Author(s)
Li, Jianning

Universitätsklinikum Essen

Zhou, Zongwei

Whiting School of Engineering

Yang, Jiancheng  

École Polytechnique Fédérale de Lausanne

Pepe, Antonio

Technische Universitat Graz

Gsaxner, Christina

Technische Universitat Graz

Luijten, Gijs

Universitätsklinikum Essen

Qu, Chongyu

Whiting School of Engineering

Zhang, Tiezheng

Whiting School of Engineering

Chen, Xiaoxi

Renji Hospital

Li, Wenxuan

Whiting School of Engineering

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Date Issued

2024

Published in
Biomedizinische Technik
Subjects

3D medical shapes

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anatomy education

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augmented reality

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benchmark

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shapeomics

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virtual reality

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Available on Infoscience
January 25, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/244280
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