Lung lesion segmentation of CT scans after SARS-CoV-2 infection: combining nonhuman primate with human data for interspecies transfer learning

Linh Shinguyen; Winston Chu; Mark Rustad; Ashkan Malayeri; Maryam Homayounieh; Shiva Singh; Claudia Calcagno; Philip Sayre; Ian Crozier; Gabriella Worwa; Jens H. Kuhn; Bradford Wood; Marcelo Castro; Jeffrey Solomon

doi:10.1117/12.3006206

2 April 2024 Lung lesion segmentation of CT scans after SARS-CoV-2 infection: combining nonhuman primate with human data for interspecies transfer learning

Linh Shinguyen, Winston Chu, Mark Rustad, Ashkan Malayeri, Maryam Homayounieh, Shiva Singh, Claudia Calcagno, Philip Sayre, Ian Crozier, Gabriella Worwa, Jens H. Kuhn, Bradford Wood, Marcelo Castro, Jeffrey Solomon

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293010 (2024) https://doi.org/10.1117/12.3006206
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Agile development of reliable and accurate segmentation models during an infectious disease outbreak has the potential to reduce the need for already-strained human expertise. Global research and data-sharing efforts during the COVID-19 pandemic have shown how rapidly Deep-Learning (DL) models can be developed when public datasets are available for training. However, these efforts have been rare, usually limited by the unavailability of Computed Tomography (CT) imaging datasets from patients in the clinical setting. In the absence of human data, animal models faithful to human disease are used to investigate the imaging phenotype of high-consequence and emerging pathogens. As simultaneous access to both human and Nonhuman Primate (NHP) data for the same respiratory infection is unusual, we were interested in whether the inclusion of NHP data might enhance DL image segmentation of lung lesions associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Thus, we set out to evaluate DL performance and generalizability to a human test set. We found that combining human and NHP data and utilizing pretrained NHP models to initialize model training outperformed a model trained solely on human CT data. By studying the interaction between human and NHP CT imaging in developing these models, we can assess the potential value of NHP datasets for known or novel viruses that emerge in settings where medical imaging capacity is limited. Understanding and leveraging NHP datasets to improve the agility and quality of model development capabilities could better prepare us to respond to disease outbreaks in the human population.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Linh Shinguyen, Winston Chu, Mark Rustad, Ashkan Malayeri, Maryam Homayounieh, Shiva Singh, Claudia Calcagno, Philip Sayre, Ian Crozier, Gabriella Worwa, Jens H. Kuhn, Bradford Wood, Marcelo Castro, and Jeffrey Solomon "Lung lesion segmentation of CT scans after SARS-CoV-2 infection: combining nonhuman primate with human data for interspecies transfer learning", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293010 (2 April 2024); https://doi.org/10.1117/12.3006206

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