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Urinary stone size estimation: a new segmentation algorithm-based CT method
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Radiology, Örebro University Hospital, Örebro, Sweden.ORCID iD: 0000-0002-1346-1450
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
Örebro University, School of Science and Technology.
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Medical Physics, Örebro University Hospital, Örebro, Sweden.ORCID iD: 0000-0002-8351-3367
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2012 (English)In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 22, no 4, p. 731-737Article in journal (Refereed) Published
Abstract [en]

The size estimation in CT images of an obstructing ureteral calculus is important for the clinical management of a patient presenting with renal colic. The objective of the present study was to develop a reader independent urinary calculus segmentation algorithm using well-known digital image processing steps and to validate the method against size estimations by several readers. Fifty clinical CT examinations demonstrating urinary calculi were included. Each calculus was measured independently by 11 readers. The mean value of their size estimations was used as validation data for each calculus. The segmentation algorithm consisted of interpolated zoom, binary thresholding and morphological operations. Ten examinations were used for algorithm optimisation and 40 for validation. Based on the optimisation results three segmentation method candidates were identified. Between the primary segmentation algorithm using cubic spline interpolation and the mean estimation by 11 readers, the bias was 0.0 mm, the standard deviation of the difference 0.26 mm and the Bland-Altman limits of agreement 0.0 +/- 0.5 mm. The validation showed good agreement between the suggested algorithm and the mean estimation by a large number of readers. The limit of agreement was narrower than the inter-reader limit of agreement previously reported for the same data. The size of kidney stones is usually estimated manually by the radiologist. An algorithm for computer-aided size estimation is introduced. The variability between readers can be reduced. A reduced variability can give better information for treatment decisions.

Place, publisher, year, edition, pages
New York, USA: Springer, 2012. Vol. 22, no 4, p. 731-737
Keywords [en]
X-ray computed tomography, ureteral calculi, kidney stone, computer-assisted image processing, computer-assisted image interpretation
National Category
Medical and Health Sciences Radiology, Nuclear Medicine and Medical Imaging
Research subject
Medicine
Identifiers
URN: urn:nbn:se:oru:diva-22493DOI: 10.1007/s00330-011-2309-xISI: 000301496900002PubMedID: 22160167Scopus ID: 2-s2.0-84861461077OAI: oai:DiVA.org:oru-22493DiVA, id: diva2:514671
Note

Funding Agency:

Knowledge Foundation, Stockholm, Sweden 

Available from: 2012-04-10 Created: 2012-04-10 Last updated: 2019-03-26Bibliographically approved
In thesis
1. The stack mode review of volumetric datasets: applications for urinary stone disease
Open this publication in new window or tab >>The stack mode review of volumetric datasets: applications for urinary stone disease
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

During the last decades the acquisition and visualization of radiological images have rapidly evolved. The increasing amounts of volumetric image data particularly from modern CT systems necessitate a constant evolution of the radiological visualization techniques.

The dominating display mode for volumetric images has been the stack mode display since its introduction in computerized image review. In the increasing amounts of image data, the stack mode display needs to be analyzed so that the information content in the high resolution datasets can be transformed into clinically relevant information for the management of the individual patient. In the present thesis some aspects of the stack mode display were analyzed using for the most part the size estimation of urinary stones in unenhanced CT as a model.

The estimated size has an important correlation to the prognosis for spontaneous passage of an obstructing ureteral stone. In the present thesis the reader variations in the size estimation of urinary stones were quantified, using different visualization parameters and after an attempt to reduce the variations with a training session for the readers. The influence on the estimated stone size of CT image post processing parameters was quantified. A segmentation algorithm was developed and demonstrated to reduce the reader variability through reader independent computer aid. One limitation of the stack mode display concerns three-dimensional shapes, which was modeled by a comparison between the estimated length and width of urinary stones in two- and three-dimensional segmentation. The increasing number of image slices in the acquisitions introduces a need for efficient navigation of the image volumes. In the present thesis the navigation of CT datasets using different user interface devices was evaluated.

The rapid evolution of the MRI and CT systems necessitates a constant refinement and evaluation of the cornerstone for radiological volumetric reviewing – the stack mode display of volumetric datasets.

Place, publisher, year, edition, pages
Örebro: Örebro universitet, 2013. p. 61
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 91
Keywords
stack mode display, image visualization, image perception, computed tomography, urinary stones, urolithiasis, PACS
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Medicine
Identifiers
urn:nbn:se:oru:diva-30324 (URN)978-91-7668-948-6 (ISBN)
Public defence
2013-10-04, Bohmanssonsalen, Universitetssjukhuset i Örebro, Örebro, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2013-08-26 Created: 2013-08-26 Last updated: 2019-03-26Bibliographically approved

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Lidén, MatsAndersson, TorbjörnBroxvall, MathiasThunberg, PerGeijer, Håkan

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