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PlatiPy: Processing Library and Analysis Toolkit for Medical Imaging in Python

2023; Open Journals; Volume: 8; Issue: 86 Linguagem: Inglês

10.21105/joss.05374

2475-9066

Phillip Chlap, Robert Finnegan,

Lung Cancer Diagnosis and Treatment

PlatiPy offers a comprehensive suite of tools and utilities for conducting medical image analysis research utilising Python.These tools include functions for converting data between the clinical standard DICOM format and the research-friendly NIfTI format, capabilities for image registration and atlas-based segmentation, and efficient image visualisation tools to facilitate rapid development in research.Additionally, the library includes auto-segmentation models developed through various research projects enabling their streamlined deployment for use in future projects by the research community. Statement of needPython has gained significant popularity in the field of medical image analysis research in recent years, due in part to its open-source nature and the support of a large community of third-party libraries.Libraries such as SimpleITK (Lowekamp et al., 2013;Yaniv et al., 2018), scikit-learn (Pedregosa et al., 2011) and pydicom (Mason et al., 2022) offer a wide range of functionality for developing medical image analysis tools.However, researchers often face the challenge of writing code to prepare data, creating wrapper functions for common procedures, and visualising images with scalar, vector or structure overlays throughout the analysis pipeline.PlatiPy addresses these challenges by providing many of these functions, eliminating the need for researchers to reinvent the wheel.