This repository contains pre-trained models and code for deep learning-based autosegmentation of swallowing and chewing structures viz. masseters (left and right),medial pterygoids (left and right), larynx, and pharyngeal constrictor muscle.
A Colab notebook demo_DLseg_swallowing_and_chewing_structures.ipynb is provided to demonstrate usage.
This codebase uses the GNU-GPL copyleft license (https://www.gnu.org/licenses/lgpl3.0.en.html) to allow open-source distribution with additional restrictions. The license retains the ability to propagate any changes to the codebase back to the opensource community along with the following restrictions (i) No Clinical Use, (ii) No Commercial Use, and (iii) Dual Licensing which reserve the right to diverge and/or modify and/or expand the model implementations library to have a closed source/proprietary version along with the open source version in future. It should be noted that the segmentation models are distributed strictly for research use. Clinical or commercial use is prohibited. CERR and containerized model implementations have not been approved by the U.S. Food and Drug Administration (FDA). The library merely provides implementations of the developed models, whereas the creators of models retain the copyright to their work.
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Iyer A, Thor M, Onochie I, Hesse J, Zakeri K, LoCastro E, Jiang J, Veeraraghavan H, Elguindi S, Lee NY, Deasy JO, and Apte AP. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology, vol. 67, no. 2, 2022, p. 024001., https://doi.org/10.1088/1361-6560/ac4000.
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Aditya P. Apte, Aditi Iyer, Maria Thor, Rutu Pandya, Rabia Haq, Jue Jiang, Eve LoCastro, Amita Shukla-Dave, Nishanth Sasankan, Ying Xiao, Yu-Chi Hu, Sharif Elguindi, Harini Veeraraghavan, Jung Hun Oh, Andrew Jackson, Joseph O. Deasy. "Library of deep-learning image segmentation and outcomes model-implementations." Physica Medica, Volume 73, 2020, Pages 190-196, ISSN 1120-1797, https://doi.org/10.1016/j.ejmp.2020.04.011.