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

Tomography

Alex M. Bassil¹  Yvonne M. Mowery¹  Rutulkumar Patel¹  Yi Qi²  Matthew D. Holbrook²  Cristian T. Badea²  Darin P. Clark² 

[1] Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA;Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA;

关键词: preclinical imaging;    machine learning (ML);    X-ray CT (CT);   

DOI  :  10.3390/tomography7030032

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown