Urinary stone size estimation: a new segmentation algorithm-based CT method.
Objectives: 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...
| Publicado en: | European Radiology Vol. 22; no. 4; pp. 731 - 738 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104534378&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104534378 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2012 vid: 22 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104534378 NLM22160167 2011490218 10.1007/s00330-011-2309-x NLM22160167 104534378 ppf: 731 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Urinary stone size estimation: a new segmentation algorithm-based CT method. aug: au: Lidén M Andersson T Broxvall M Thunberg P Geijer H Lidén, Mats Andersson, Torbjörn Broxvall, Mathias Thunberg, Per Geijer, Håkan affil: School of Health and Medical Sciences, Örebro University, S-701 82, Örebro, Sweden sug: subj: Algorithms Information Science Methods Radiographic Image Interpretation, Computer-Assisted Methods Urinary Calculi Radiography Urography Methods Human Observer Bias Radiographic Image Enhancement Methods Reproducibility of Results Sensitivity and Specificity ab: Objectives: 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.Methods: 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.Results: 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.Conclusions: 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.Key Points: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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