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Background and Objective: Deep learning enables tremendous progress inmedical image analysis. One driving force of this progress are open-sourceframeworks like TensorFlow and PyTorch. However, these frameworks rarelyaddress issues specific to the domain of medical image analysis, such as 3-Ddata handling and distance metrics for evaluation. pymia, an open-source Pythonpackage, tries to address these issues by providing flexible data handling andevaluation independent of the deep learning framework.  Methods: The pymia package provides data handling and evaluationfunctionalities. The data handling allows flexible medical image handling inevery 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 beintegrated into deep learning pipelines. The evaluation allows stand-aloneresult calculation and reporting, as well as performance monitoring duringtraining 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 evaluationmethods. While data handling and evaluation are independent of the deeplearning framework used, they can easily be integrated into TensorFlow andPyTorch pipelines. The developed package was successfully used in a variety ofresearch projects for segmentation, reconstruction, and regression.  Conclusions: The pymia package fills the gap of current deep learningframeworks regarding data handling and evaluation in medical image analysis. Itis available at https://github.com/rundherum/pymia and can directly beinstalled from the Python Package Index using pip install pymia.

pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis