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Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Medical Sciences. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.ORCID iD: 0000-0001-8949-119X
Örebro University, School of Medical Sciences. Department of Medical Physics, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-8351-3367
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-3122-693X
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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. Vol. 97, p. 153-160
Keywords [en]
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: urn:nbn:se:oru:diva-67139DOI: 10.1016/j.compbiomed.2018.04.021ISI: 000435623700015PubMedID: 29730498Scopus ID: 2-s2.0-85046800526OAI: oai:DiVA.org:oru-67139DiVA, id: diva2:1212942
Note

Funding Agencies:

Nyckelfonden  OLL-597511 

Vinnova under the project "Interactive Deep Learning for 3D image analysis"  

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2024-01-16Bibliographically approved

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Längkvist, MartinJendeberg, JohanThunberg, PerLoutfi, AmyLidén, Mats

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