Literature DB >> 9087371

Evaluation of manual vs semi-automated delineation of liver lesions on CT images.

E Bellon1, M Feron, F Maes, L V Hoe, D Delaere, F Haven, S Sunaert, A L Baert, G Marchal, P Suetens.   

Abstract

In this paper we compare a semi-automated delineation method with totally manual delineation for area quantification, with respect to efficiency, quality, and intra- and interobserver variability. Liver lesions on 28 CT images were delineated by three observers, twice using completely manual delineation and twice using a semi-automated method. Quantitative comparisons were performed with respect to delineated area and time required for the delineation tasks. Subjective comparisons were performed with respect to efficiency and perceived quality of the semi-automated method. The areas obtained using semi-automated delineation were significantly smaller (11 %) than those obtained using totally manual delineation. Intraobserver and interobserver variability with the semi-automated method were approximately three times lower than with manual delineation. Efficiency of the semi-automated method was subjectively rated favorable, although further improvements are possible. With respect to quality, the semi-automated method was ranked better than the manual method in 73 % of cases.

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Year:  1997        PMID: 9087371     DOI: 10.1007/s003300050180

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  4 in total

1.  Reproducibility of linear tumor measurements using PACS: comparison of caliper method with edge-tracing method.

Authors:  Wayne L Monsky; Vassilios Raptopoulos; Mary T Keogan; David Doty; Ihab Kamel; Chun Sam Yam; Bernard J Ransil
Journal:  Eur Radiol       Date:  2003-12-05       Impact factor: 5.315

2.  Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics.

Authors:  Shonket Ray; Rosalie Hagge; Marijo Gillen; Miguel Cerejo; Shidrokh Shakeri; Laurel Beckett; Tamara Greasby; Ramsey D Badawi
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

3.  Semiautomatic segmentation of liver metastases on volumetric CT images.

Authors:  Jiayong Yan; Lawrence H Schwartz; Binsheng Zhao
Journal:  Med Phys       Date:  2015-11       Impact factor: 4.071

4.  Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network.

Authors:  Trong-Ngoc Le; Pham The Bao; Hieu Trung Huynh
Journal:  Biomed Res Int       Date:  2016-08-14       Impact factor: 3.411

  4 in total

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