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Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning
We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8396172/ https://www.ncbi.nlm.nih.gov/pubmed/34449750 http://dx.doi.org/10.3390/tomography7030032 |
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author | Holbrook, Matthew D. Clark, Darin P. Patel, Rutulkumar Qi, Yi Bassil, Alex M. Mowery, Yvonne M. Badea, Cristian T. |
author_facet | Holbrook, Matthew D. Clark, Darin P. Patel, Rutulkumar Qi, Yi Bassil, Alex M. Mowery, Yvonne M. Badea, Cristian T. |
author_sort | Holbrook, Matthew D. |
collection | PubMed |
description | We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice. |
format | Online Article Text |
id | pubmed-8396172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83961722021-08-28 Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning Holbrook, Matthew D. Clark, Darin P. Patel, Rutulkumar Qi, Yi Bassil, Alex M. Mowery, Yvonne M. Badea, Cristian T. Tomography Article We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice. MDPI 2021-08-07 /pmc/articles/PMC8396172/ /pubmed/34449750 http://dx.doi.org/10.3390/tomography7030032 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Holbrook, Matthew D. Clark, Darin P. Patel, Rutulkumar Qi, Yi Bassil, Alex M. Mowery, Yvonne M. Badea, Cristian T. Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title | Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title_full | Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title_fullStr | Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title_full_unstemmed | Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title_short | Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning |
title_sort | detection of lung nodules in micro-ct imaging using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8396172/ https://www.ncbi.nlm.nih.gov/pubmed/34449750 http://dx.doi.org/10.3390/tomography7030032 |
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