Pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis. (January 2021)

• Record Type: Journal Article
• Authors: Jungo, Alain; Scheidegger, Olivier; Reyes, Mauricio; Balsiger, Fabian
• Is Part Of: Computer methods and programs in biomedicine. Volume 198(2021)
• Publication Date: 2021-01
• Subjects: Medical image analysis -- Deep learning -- Data handling -- Evaluation -- Metrics; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105796
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Open-source Python-based data handling and evaluation for medical image analysis with deep learning. Flexible data handling (2-D, 3-D; full- or patch-wise) independent of the deep learning framework. Smooth integration into the current deep learning frameworks (TensorFlow and PyTorch) and simple switch of framework for prototyping. Evaluation functionalities for stand-alone result calculation and reporting (CSV files, console) and monitoring of the training progress. Vast amount of domain-specific metrics for image segmentation, image reconstruction, and regression. 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 .