ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Comput Methods Programs Biomed. 2021 Jan;198:105796. doi: 10.1016/j.cmpb.2020.105796. Epub 2020 Oct 19.
Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues specific to the domain of medical image analysis, such as 3-D data handling and distance metrics for evaluation. pymia, an open-source Python package, tries to address these issues by providing flexible data handling and evaluation independent of the deep learning framework.
The pymia package provides data handling and evaluation functionalities. The data handling allows flexible medical image handling in every commonly used format (e.g., 2-D, 2.5-D, and 3-D; full- or patch-wise). Even data beyond images like demographics or clinical reports can easily be integrated into deep learning pipelines. The evaluation allows stand-alone result calculation and reporting, as well as performance monitoring during training using a vast amount of domain-specific metrics for segmentation, reconstruction, and regression.
The pymia package is highly flexible, allows for fast prototyping, and reduces the burden of implementing data handling routines and evaluation methods. While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines. The developed package was successfully used in a variety of research projects for segmentation, reconstruction, and regression.
The pymia package fills the gap of current deep learning frameworks regarding data handling and evaluation in medical image analysis. It is available at https://github.com/rundherum/pymia and can directly be installed from the Python Package Index using pip install pymia.
深度学习在医学图像分析中取得了巨大的进展。推动这一进展的一个动力是像 TensorFlow 和 PyTorch 这样的开源框架。然而，这些框架很少解决医学图像分析领域的特定问题，如 3D 数据处理和评估的距离度量。pymia 是一个开源的 Python 包，试图通过提供灵活的数据处理和与深度学习框架无关的评估来解决这些问题。
pymia 包提供了数据处理和评估功能。数据处理允许以每一种常用格式（例如 2D、2.5D 和 3D；全幅或部分幅）灵活地处理医学图像。甚至像人口统计学或临床报告这样的数据也可以很容易地集成到深度学习管道中。评估允许独立计算和报告结果，以及在训练期间使用大量特定于领域的分割、重建和回归度量来进行性能监测。
pymia 包非常灵活，允许快速原型制作，并减少了实现数据处理例程和评估方法的负担。虽然数据处理和评估与使用的深度学习框架无关，但它们可以很容易地集成到 TensorFlow 和 PyTorch 管道中。开发的包在各种分割、重建和回归的研究项目中得到了成功的应用。
pymia 包填补了当前深度学习框架在医学图像分析中数据处理和评估方面的空白。它可以在 https://github.com/rundherum/pymia 上获得，并可以使用 pip install pymia 直接从 Python 包索引中安装。
