SliCERR: An Extension for 3D Slicer to Execute CERR Analysis Routines
E LoCastro1*, H Veeraraghavan2, T Kapur3, A Iyer4, C Pinter5, G Sharp6, J Deasy7, A Apte8, (1) Memorial Sloan Kettering Cancer Center, New York, New York, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Harvard Medical School, Boston, MA, (4) Memorial Sloan Kettering Cancer Center, New York, NY, (5) Queen's University, Kingston, ON, CA, (6) Massachusetts General Hospital, Beverly, MA, (7) Memorial Sloan Kettering Cancer Center, New York, NY, (8) Memorial Sloan-Kettering Cancer Center, Maywood, NJ
Presentations
PO-GePV-M-23 (Sunday, 7/10/2022) [Eastern Time (GMT-4)]
ePoster Forums
Purpose: sliCERR has been developed as an extension for 3D Slicer to execute CERR's radiotherapy and image analysis functionality, and vice versa to import datasets from 3D Slicer into CERR, the Octave/MATLAB-based software platform for radiation therapy treatment planning and imaging informatics. sliCERR will provide modules for data input/output (I/O) conversion operations and run analysis routines from CERR.
Methods: sliCERR is an extension for 3D Slicer, written in Python. The cerr2mrml module handles the I/O of loading native CERR planC format files into the 3D Slicer MRML scene, including import of scan, dose and ROI contours. Use of recent developments in CERR such as Deep Learning-based image segmentation and radiomics texture mapping, the ROE Radiotherapy Outcomes Estimator and semi-quantitative DCE features are demonstrated in Jupyter notebooks publicly available on GitHub. Computational results from CERR routines can be pulled into 3D Slicer using cerr2mrml functionality. In order to use sliCERR, users must download the CERR codebase, publicly available on GitHub (http://github.com/cerr/cerr, octave-dev branch). Users must also have a local installation of GNU Octave (v6+); in addition to the oct2py and octave_kernel packages in the Slicer Python interpreter via the pip_install feature. The sliCERR extension is platform-agnostic and will work on Windows, MacOS or Linux.
Results: The sliCERR extension is available for preview on GitHub (http://github.com/cerr/sliCERR) and on release will be directly available from the Slicer Extension Manager. To use Jupyter demonstration notebooks, the SlicerJupyter extension must be installed via 3D Slicer. GUI is in development. Documentation for setup and usage is available on the repository wiki page.
Conclusion: We present work that makes it efficient to pass image data between CERR treatment planning and radiotherapy analysis routines and the widely adopted 3D Slicer platform, which served over a million unique downloads in the last year.
Keywords
Treatment Planning, Dose Volume Histograms, Computer Software
Taxonomy
IM/TH- Image Analysis Skills (broad expertise across imaging modalities): Image processing
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