# MedShapeNetCore: [[GitHub](https://github.com/Jianningli/medshapenet-feedback/tree/main), [Release page](https://pypi.org/project/MedShapeNetCore/), [Zenodo](https://zenodo.org/records/10423181), [Publication](https://arxiv.org/abs/2308.16139)]
MedShapeNetCore is a subset of [MedShapeNet](https://arxiv.org/abs/2308.16139), containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in *npz* format ([Zenodo](https://zenodo.org/records/10423181)).
This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows.
# Installation (Python >=3.8, [Release page](https://pypi.org/project/MedShapeNetCore/))
pip install MedShapeNetCore
or install from source:
python setup.py install
# Getting started ([](https://colab.research.google.com/github/Jianningli/medshapenet-feedback/blob/main/pip_install_MedShapeNetCore/getting_started.ipynb))
basic commands:
python -m MedShapeNetCore info # check the general information of the dataset
python -m MedShapeNetCore download DATASET # download a dataset (replace DATASETA with the one you want to download e.g., ASOCA)
python -m MedShapeNetCore check_available_keys DATASET # check the available keys of the DATASET
how to import module functions in python:
from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer
For more commands and detailed usage, please refer to the colab [notebook](https://colab.research.google.com/github/Jianningli/medshapenet-feedback/blob/main/pip_install_MedShapeNetCore/getting_started.ipynb).
# Use MedShapeNetCore in Machine Learning Workflows (Minimal Reproducible Example)
* 3D Shape Classification with MONAI [](https://colab.research.google.com/github/Jianningli/medshapenet-feedback/blob/main/pip_install_MedShapeNetCore/examples/MONAI_3D_Shape_Classification.ipynb)
* 3D Shape Classification with Tensorflow [](https://colab.research.google.com/github/Jianningli/medshapenet-feedback/blob/main/pip_install_MedShapeNetCore/examples/Tensorflow_3D_Shape_Classification.ipynb)
# Reference
```
@article{li2023medshapenet,
title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision},
author={Li, Jianning and Pepe, Antonio and Gsaxner, Christina and Luijten, Gijs and Jin, Yuan and Ambigapathy, Narmada and Nasca, Enrico and Solak, Naida and Melito, Gian Marco and Memon, Afaque R and others},
journal={arXiv preprint arXiv:2308.16139},
year={2023}}
```
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