论文标题
Pymia：基于深度学习的医学图像分析中用于数据处理和评估的Python软件包
pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
论文作者
论文摘要
背景和目标：深度学习在医学图像分析中可以取得巨大进展。这种进度的一个驱动力是Tensorflow和Pytorch等开源框架。但是，这些框架很少解决医学图像分析领域的特定问题，例如3-D数据处理和距离指标进行评估。 Pymia是一种开源Python软件包，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；全斑或贴剂）进行灵活的医疗图像处理。甚至超出人口统计图像或临床报告之类的图像之外的数据也可以轻松地集成到深度学习管道中。该评估允许独立的结果计算和报告，以及在培训期间使用大量域特异性指标进行分割，重建和回归的训练过程。 结果：PYMIA软件包具有高度灵活性，可以快速进行原型制作，并减轻了实施数据处理程序和评估方法的负担。尽管数据处理和评估与所使用的深度学习框架无关，但它们可以很容易地集成到张量和pytorch管道中。开发的软件包成功用于各种研究项目，以进行细分，重建和回归。 结论：Pymia软件包填补了有关医学图像分析中有关数据处理和评估的当前深度学习框架的空白。它可在https://github.com/rundherum/pymia上获得，可以使用PIP Install Pymia直接从Python软件包索引安装。
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.
