Background and Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

背景与目的：深度学习在医学图像分析方面取得了巨大的进步。这种进步的驱动力之一是TensorFlow和PyTorch等开源框架。但是，这些框架很少解决特定于医学图像分析领域的问题，例如3-D数据处理和用于评估的距离度量。pymia是一个开放源代码的Python软件包，它试图通过提供独立于深度学习框架的灵活数据处理和评估来解决这些问题。

方法： Pymia软件包提供了数据处理和评估功能。数据处理允许以每种常用格式（例如2-D，2.5-D和3-D；完整或逐块）灵活地处理医学图像。甚至人口统计或临床报告等图像之外的数据也可以轻松集成到深度学习管道中。评估允许独立的结果计算和报告，以及在训练期间使用大量领域特定的指标进行细分，重建和回归的性能监控。

结果： Pymia软件包具有高度的灵活性，可以快速进行原型制作，并减轻了实施数据处理例程和评估方法的负担。尽管数据处理和评估与所使用的深度学习框架无关，但它们可以轻松集成到TensorFlow和PyTorch管道中。所开发的软件包已成功用于各种研究项目中，以进行细分，重建和回归。

结论： Pymia软件包填补了当前深度学习框架在医学图像分析中有关数据处理和评估的空白。它可以从https://github.com/rundherum/pymia获得，并且可以使用pip install pymia从Python软件包索引直接安装。