Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning
Abstract
<jats:p>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.</jats:p>
Type
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https://hdl.handle.net/10161/23575Published Version (Please cite this version)
10.3390/tomography7030032Publication Info
Holbrook, Matthew D; Clark, Darin P; Patel, Rutulkumar; Qi, Yi; Bassil, Alex M; Mowery,
Yvonne M; & Badea, Cristian T (n.d.). Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning. Tomography, 7(3). pp. 358-372. 10.3390/tomography7030032. Retrieved from https://hdl.handle.net/10161/23575.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Cristian Tudorel Badea
Professor in Radiology
Our lab's research focus lies primarily in developing novel quantitative imaging systems,
reconstruction algorithms and analysis methods. My major expertise is in preclinical
CT.
Currently, we are particularly interested in developing novel strategies for spectral
CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy
and diagnostics).
We are also engaged in developin
Darin Clark
Assistant Professor in Radiology
Yvonne Marie Mowery
Butler Harris Assistant Professor in Radiation Oncology
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