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Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed
Örebro University, School of Medical Sciences.ORCID iD: 0000-0002-1346-1450
Örebro University, School of Medical Sciences.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
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2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Purpose: To develop a deep learning method for assisting radiologists in the discrimination between distal ureteral stones and pelvic phleboliths in thin slice CT images, and to evaluate whether this differentiation is possible using only local features.

Methods and materials: A limited field-of-view image data bank was retrospectively created, consisting of 5x5x5 cm selections from 1 mm thick unenhanced CT images centered around 218 pelvis phleboliths and 267 distal ureteral stones in 336 patients. 50 stones and 50 phleboliths formed a validation cohort and the remainder a training cohort. Ground truth was established by a radiologist using the complete CT examination during inclusion.The limited field-of-view CT stacks were independently reviewed and classified as containing a distal ureteral stone or a phlebolith by seven radiologists. Each cropped stack consisted of 50 slices (5x5 cm field-of-view) and was displayed in a standard PACS reading environment. A convolutional neural network using three perpendicular images (2.5D-CNN) from the limited field-of-view CT stacks was trained for classification.

Results: The 2.5D-CNN obtained 89% accuracy (95% confidence interval 81%-94%) for the classification in the unseen validation cohort while the accuracy of radiologists reviewing the same cohort was 86% (range 76%-91%). There was no statistically significant difference between 2.5D-CNN and radiologists.

Conclusion: The 2.5D-CNN achieved radiologist level classification accuracy between distal ureteral stones and pelvic phleboliths when only using the local features. The mean accuracy of 86% for radiologists using limited field-of-view indicates that distant anatomical information that helps identifying the ureter’s course is needed.

Place, publisher, year, edition, pages
2018.
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:oru:diva-67372OAI: oai:DiVA.org:oru-67372DiVA, id: diva2:1221798
Conference
European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved

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Lidén, MatsJendeberg, JohanLängkvist, MartinLoutfi, AmyThunberg, Per

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