MedShapeNetCore

MedShapeNetCore is a subset of MedShapeNet, 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). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository. If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet. Make sure to check out the MedShapeNetCore Show Case for the latest update. @article{li2023medshapenet, title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision}, author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others}, journal={arXiv preprint arXiv:2308.16139}, year={2023} } To install the MedShapeNetCore python package: pip install MedShapeNetCore You can search the database using anatomy nomenclature, such as liver, aorta, skull, instrument etc. Fore more commands, visit the Github Repository. python -m MedShapeNetCore search_by_organ ORGAN You can also directly download .stl files using the following command (replace ORGAN with the organ you want to search, e.g., liver, skull): python -m MedShapeNetCore search_and_download ORGAN Check the available .npz files: python -m MedShapeNetCore info Download .npz files to be used in python: python -m MedShapeNetCore download FaceVRImport the python packages to load, visualize and process the .npz files:from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer Import shape dataloader:from MedShapeNetCore.MedShapeNetCore import BatchLoaderfacial_point=BatchLoader('facialVR','point',batch_size=2,shuffle=True) for batch in facial_point: print(batch.shape) Use the fast load function to load a specified number of samples (2) of a given format ('point') from a dataset ('facialVR'): msn_loader=MSNLoader()data=msn_loader.fast_load('facialVR','point',2,shuffle=True) print(data.shape)MedShapeNetCore是MedShapeNet的子集，包含更轻量化的3D解剖形态数据，格式涵盖掩码（mask）、点云（point cloud）与网格（mesh）。形态数据以NumPy数组形式存储于嵌套字典中，采用npz格式存档（托管于Zenodo）。本API提供了通过Python下载、访问与处理该形态数据的途径，可将MedShapeNetCore无缝集成至基于Python的机器学习工作流中。如需了解详细信息，请访问其GitHub仓库。若您在研究中使用该数据集，请除引用MedShapeNet外，一并引用其原始数据集（可通过执行命令`python -m MedShapeNetCore info`查询获取引用信息）。 请务必查看MedShapeNetCore展示专区以获取最新更新。 @article{li2023medshapenet, title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision}, author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others}, journal={arXiv preprint arXiv:2308.16139}, year={2023} } 若需安装MedShapeNetCore Python包，请执行以下命令： pip install MedShapeNetCore 您可通过解剖学术语（例如肝脏、主动脉、颅骨、手术器械等）检索数据库。更多命令请访问其GitHub仓库。 命令示例： python -m MedShapeNetCore search_by_organ ORGAN 您也可通过以下命令直接下载.stl文件（请将ORGAN替换为目标器官名称，例如肝脏、颅骨）： python -m MedShapeNetCore search_and_download ORGAN 查看可用的.npz文件： python -m MedShapeNetCore info 下载供Python使用的.npz文件： python -m MedShapeNetCore download FaceVR 导入用于加载、可视化与处理.npz文件的Python包： from MedShapeNetCore.MedShapeNetCore import MyDict, MSNLoader, MSNVisualizer, MSNSaver, MSNTransformer 导入形态数据加载器： from MedShapeNetCore.MedShapeNetCore import BatchLoader facial_point = BatchLoader('facialVR', 'point', batch_size=2, shuffle=True) for batch in facial_point: print(batch.shape) 使用快速加载函数从指定数据集（`facialVR`）中加载指定数量（2个）、指定格式（点云）的样本： msn_loader = MSNLoader() data = msn_loader.fast_load('facialVR', 'point', 2, shuffle=True) print(data.shape)