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Deep Learning for Automatic Real-time Pulmonary Nodule Detection and Quantitative Analysis

dc.contributor.advisor Yin, Fang-Fang
dc.contributor.author Liu, Chenyang
dc.date.accessioned 2019-06-07T19:51:14Z
dc.date.available 2020-06-05T08:17:12Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10161/18883
dc.description Master's thesis
dc.description.abstract <p>Purpose: To develop a novel computer-aided diagnosis (CAD) pulmonary nodule detection system that can not only perform real-time detection but also characterize quantitative nodule information based on deep learning methods.</p><p>Method: We constructed a convolutional neural network (CNN) for automated pulmonary nodule detection and characterization. Nodule detection was accomplished by customizing a detection algorithm (YOLO v3), which comprised of a feature extractor and a bounding box generator. The feature extractor had 19 convolutional layers with 7 residual shortcut connections to extract features on input images at three different down-sampling scales (i.e. 4, 8, and 16). The bounding box generator had 7 convolutional layers to determine the location and size of each detected nodule. A python-based characterization system was then developed to characterize size, diameter, and central coordinates of each detected nodule within the generated bounding box. This characterization system applied a non-maximum suppression algorithm to exclude nodules below true positive probability threshold. The system was trained and validated using ten-fold cross-validation with 300 CT scans from XCAT simulation and 888 patient CT scans from LIDC–IDRI public dataset, separately. System performance was evaluated using Free-Response Receiver Operating Characteristic (FROC) analysis, competition performance metric (CPM) score, as well as precision analysis of central coordinates and diameters. </p><p>Result: The developed CAD system achieved CPM scores of 0.99 in the simulation image study and 0.873 in the public database study. The average performance time per image was less than 0.1 second. Compared with ground truth data, the detection precision in diameter were 0.26 mm using simulated images and 1.05 mm using public database, while the precision in central coordinate were 0.76 mm and 1.44 mm, respectively. </p><p>Conclusion: Preliminary evaluation showed that our proposed CAD system using deep learning methods was robust and achieved real-time nodule detection with high accuracy and characterization with high precision.</p>
dc.subject Physics
dc.subject Medical imaging
dc.subject Computer science
dc.subject computer-aided diagnosis
dc.subject deep learning
dc.subject pulmonary nodule detection
dc.title Deep Learning for Automatic Real-time Pulmonary Nodule Detection and Quantitative Analysis
dc.type Master's thesis
dc.department Medical Physics DKU
duke.embargo.months 12


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