Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.

Journal Article (Journal 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.

Full Text

Duke Authors

Cited Authors

  • Holbrook, MD; Clark, DP; Patel, R; Qi, Y; Bassil, AM; Mowery, YM; Badea, CT

Published Date

  • August 7, 2021

Published In

Volume / Issue

  • 7 / 3

Start / End Page

  • 358 - 372

PubMed ID

  • 34449750

Pubmed Central ID

  • PMC8396172

Electronic International Standard Serial Number (EISSN)

  • 2379-139X

Digital Object Identifier (DOI)

  • 10.3390/tomography7030032


  • eng

Conference Location

  • Switzerland