Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection

Natalia M. Jenuwine; Sunny N. Mahesh; Jacob D. Furst; Daniela S. Raicu

doi:10.1117/12.2293918

27 February 2018 Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection

Natalia M. Jenuwine, Sunny N. Mahesh, Jacob D. Furst, Daniela S. Raicu

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057539 (2018) https://doi.org/10.1117/12.2293918
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Early detection of lung nodules from CT scans is key to improving lung cancer treatment, but poses a significant challenge for radiologists due to the high throughput required of them. Computer-Aided Detection (CADe) systems aim to automatically detect these nodules with computer algorithms, thus improving diagnosis. These systems typically use a candidate selection step, which identifies all objects that resemble nodules, followed by a machine learning classifier which separates true nodules from false positives. We create a CADe system that uses a 3D convolutional neural network (CNN) to detect nodules in CT scans without a candidate selection step. Using data from the LIDC database, we train a 3D CNN to analyze subvolumes from anywhere within a CT scan and output the probability that each subvolume contains a nodule. Once trained, we apply our CNN to detect nodules from entire scans, by systematically dividing the scan into overlapping subvolumes which we input into the CNN to obtain the corresponding probabilities. By enabling our network to process an entire scan, we expect to streamline the detection process while maintaining its effectiveness. Our results imply that with continued training using an iterative training scheme, the one-step approach has the potential to be highly effective.

Citation Download Citation

Natalia M. Jenuwine, Sunny N. Mahesh, Jacob D. Furst, and Daniela S. Raicu "Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057539 (27 February 2018); https://doi.org/10.1117/12.2293918

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