Towards deep learning detection of lung nodules using micro-CT

M. D. Holbrook; D. P. Clark; R. Patel; Y. Qi; Y. M. Mowery; C. T. Badea

doi:10.1117/12.2581120

15 February 2021 Towards deep learning detection of lung nodules using micro-CT

M. D. Holbrook, D. P. Clark, R. Patel, Y. Qi, Y. M. Mowery, C. T. Badea

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000I (2021) https://doi.org/10.1117/12.2581120
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

We are developing imaging methods for the preclinical arm of a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform in vivo micro-CT of mouse lungs to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast and accurate approach to lung nodule segmentation. We examine the performance of DL lung tumor detection using realistically simulated nodules inserted into temporally-resolved real micro-CT datasets. Our simulations suggest that DL is a promising approach for fast, accurate segmentation of lung nodules in mice.

Conference Presentation

Citation Download Citation

M. D. Holbrook, D. P. Clark, R. Patel, Y. Qi, Y. M. Mowery, and C. T. Badea "Towards deep learning detection of lung nodules using micro-CT", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000I (15 February 2021); https://doi.org/10.1117/12.2581120

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