  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.
