RT Journal Article
SR Electronic
T1 Portable framework to deploy deep learning segmentation models for medical images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.17.435903
DO 10.1101/2021.03.17.435903
A1 Aditi Iyer
A1 Eve Locastro
A1 Aditya P. Apte
A1 Harini Veeraraghavan
A1 Joseph O. Deasy
YR 2021
UL http://biorxiv.org/content/early/2021/03/19/2021.03.17.435903.abstract
AB Purpose This work presents a framework for deployment of deep learning image segmentation models for medical images across different operating systems and programming languages.Methods Computational Environment for Radiological Research (CERR) platform was extended for deploying deep learning-based segmentation models to leverage CERR’s existing functionality for radiological data import, transformation, management, and visualization. The framework is compatible with MATLAB as well as GNU Octave and Python for license-free use. Pre and post processing configurations including parameters for pre-processing images, population of channels, and post-processing segmentations was standardized using JSON format. CPU and GPU implementations of pre-trained deep learning segmentation models were packaged using Singularity containers for use in Linux and Conda environment archives for Windows, macOS and Linux operating systems. The framework accepts images in various formats including DICOM and CERR’s planC and outputs segmentation in various formats including DICOM RTSTRUCT and planC objects. The ability to access the results readily in planC format enables visualization as well as radiomics and dosimetric analysis. The framework can be readily deployed in clinical software such as MIM via their extensions.Results The open-source, GPL copyrighted framework developed in this work has been successfully used to deploy Deep Learning based segmentation models for five in-house developed and published models. These models span various treatment sites (H&N, Lung and Prostate) and modalities (CT, MR). Documentation for their usage and demo workflow is provided at https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. The framework has also been used in clinical workflow for segmenting images for treatment planning and for segmenting publicly available large datasets for outcomes studies.Conclusions This work presented a comprehensive, open-source framework for deploying deep learning-based medical image segmentation models. The framework was used to translate the developed models to clinic as well as reproducible and consistent image segmentation across institutions, facilitating multi-institutional outcomes modeling studies.Competing Interest StatementThe authors have declared no competing interest.