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2018 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 97, p. 153-160Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Computer aided detection, Ureteral stone, Convolutional neural networks, Computed tomography, Training set selection, False positive reduction
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:oru:diva-67139 (URN)10.1016/j.compbiomed.2018.04.021 (DOI)000435623700015 ()29730498 (PubMedID)2-s2.0-85046800526 (Scopus ID)
Note
Funding Agencies:
Nyckelfonden OLL-597511
Vinnova under the project "Interactive Deep Learning for 3D image analysis"
2018-06-042018-06-042018-08-30Bibliographically approved