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

Jungo, Alain; Scheidegger, Olivier; Reyes, Mauricio; Balsiger, Fabian (2021). pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis. Computer methods and programs in biomedicine, 198, p. 105796. Elsevier 10.1016/j.cmpb.2020.105796

[img]
Preview
Text
1-s2.0-S0169260720316291-main.pdf - Published Version
Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND).

Download (1MB) | Preview

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Jungo, Alain; Scheidegger, Olivier; Reyes, Mauricio and Balsiger, Fabian

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering

ISSN:

0169-2607

Publisher:

Elsevier

Language:

English

Submitter:

Fabian Balsiger

Date Deposited:

20 Nov 2020 17:05

Last Modified:

22 Nov 2020 02:49

Publisher DOI:

10.1016/j.cmpb.2020.105796

PubMed ID:

33137700

BORIS DOI:

10.7892/boris.147477

URI:

https://boris.unibe.ch/id/eprint/147477

Actions (login required)

Edit item Edit item
Provide Feedback