Alain Jungo1, Olivier Scheidegger2, Mauricio Reyes3, Fabian Balsiger4. 1. ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. Electronic address: alain.jungo@artorg.unibe.ch. 2. 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. 3. ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. 4. ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. Electronic address: fabian.balsiger@artorg.unibe.ch.
Abstract
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.
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.
Authors: Florian Kofler; Ivan Ezhov; Lucas Fidon; Carolin M Pirkl; Johannes C Paetzold; Egon Burian; Sarthak Pati; Malek El Husseini; Fernando Navarro; Suprosanna Shit; Jan Kirschke; Spyridon Bakas; Claus Zimmer; Benedikt Wiestler; Bjoern H Menze Journal: Front Neurosci Date: 2021-12-30 Impact factor: 5.152