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Urinary stone size estimation: a new segmentation algorithm-based CT method
Örebro universitet, Institutionen för hälsovetenskap och medicin. Department of Radiology, Örebro University Hospital, Örebro, Sweden.ORCID-id: 0000-0002-1346-1450
Örebro universitet, Institutionen för hälsovetenskap och medicin.
Örebro universitet, Institutionen för naturvetenskap och teknik.
Örebro universitet, Institutionen för hälsovetenskap och medicin. Department of Medical Physics, Örebro University Hospital, Örebro, Sweden.ORCID-id: 0000-0002-8351-3367
Visa övriga samt affilieringar
2012 (Engelska)Ingår i: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 22, nr 4, s. 731-737Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
New York, USA: Springer, 2012. Vol. 22, nr 4, s. 731-737
Nyckelord [en]
X-ray computed tomography, ureteral calculi, kidney stone, computer-assisted image processing, computer-assisted image interpretation
Nationell ämneskategori
Medicin och hälsovetenskap Radiologi och bildbehandling
Forskningsämne
Medicin
Identifikatorer
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
Anmärkning

Funding Agency:

Knowledge Foundation, Stockholm, Sweden 

Tillgänglig från: 2012-04-10 Skapad: 2012-04-10 Senast uppdaterad: 2019-03-26Bibliografiskt granskad
Ingår i avhandling
1. The stack mode review of volumetric datasets: applications for urinary stone disease
Öppna denna publikation i ny flik eller fönster >>The stack mode review of volumetric datasets: applications for urinary stone disease
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Örebro: Örebro universitet, 2013. s. 61
Serie
Örebro Studies in Medicine, ISSN 1652-4063 ; 91
Nyckelord
stack mode display, image visualization, image perception, computed tomography, urinary stones, urolithiasis, PACS
Nationell ämneskategori
Radiologi och bildbehandling
Forskningsämne
Medicin
Identifikatorer
urn:nbn:se:oru:diva-30324 (URN)978-91-7668-948-6 (ISBN)
Disputation
2013-10-04, Bohmanssonsalen, Universitetssjukhuset i Örebro, Örebro, 09:00 (Svenska)
Opponent
Handledare
Tillgänglig från: 2013-08-26 Skapad: 2013-08-26 Senast uppdaterad: 2023-05-22Bibliografiskt granskad

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Lidén, MatsAndersson, TorbjörnBroxvall, MathiasThunberg, PerGeijer, Håkan

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