Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 97, Issue -, Pages 153-160Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2018.04.021
Keywords
Computer aided detection; Ureteral stone; Convolutional neural networks; Computed tomography; Training set selection; False positive reduction
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Funding
- Nyckelfonden [OLL-597511]
- Vinnova under the project Interactive Deep Learning for 3D image analysis
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Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.
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