Literature DB >> 17278490

Automated matching and segmentation of lymphoma on serial CT examinations.

Jiayong Yan1, Binsheng Zhao, Sean Curran, Andrew Zelenetz, Lawrence H Schwartz.   

Abstract

In patients with lymphoma, identification and quantification of the tumor extent on serial CT examinations is critical for assessing tumor response to therapy. In this paper, we present a computer method to automatically match and segment lymphomas in follow-up CT images. The method requires that target lymph nodes in baseline CT images be known. A fast, approximate alignment technique along the x, y, and axial directions is developed to provide a good initial condition for the subsequent fast free form deformation (FFD) registration of the baseline and the follow-up images. As a result of the registration, the deformed lymph node contours from the baseline images are used to automatically determine internal and external markers for the marker-controlled watershed segmentation performed in the follow-up images. We applied this automated registration and segmentation method retrospectively to 29 lymph nodes in 9 lymphoma patients treated in a clinical trial at our cancer center. A radiologist independently delineated all lymph nodes on all slices in the follow-up images and his manual contours served as the "gold standard" for evaluation of the method. Preliminary results showed that 26/29 (89.7%) lymph nodes were correctly matched; i.e., there was a geometrical overlap between the deformed lymph node from the baseline and its corresponding mass in the follow-up images. Of the matched 26 lymph nodes, 22 (84.6%) were successfully segmented; for these 22 lymph nodes, several metrics were calculated to quantify the method's performance. Among them, the average distance and the Hausdorff distance between the contours generated by the computer and those generated by the radiologist were 0.9 mm (stdev. 0.4 mm) and 3.9 mm (stdev. 2.1 mm), respectively.

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Year:  2007        PMID: 17278490     DOI: 10.1118/1.2404617

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  5 in total

1.  Automated temporal tracking and segmentation of lymphoma on serial CT examinations.

Authors:  Jiajing Xu; Hayit Greenspan; Sandy Napel; Daniel L Rubin
Journal:  Med Phys       Date:  2011-11       Impact factor: 4.071

2.  Computer-assisted quantitative evaluation of therapeutic responses for lymphoma using serial PET/CT imaging.

Authors:  Xin Gao; Zhong Xue; Jiong Xing; Daniel Y Lee; Stephen M Gottschalk; Helen E Heslop; Catherine M Bollard; Stephen T C Wong
Journal:  Acad Radiol       Date:  2010-01-12       Impact factor: 3.173

3.  Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest.

Authors:  Jiamin Liu; Joanne Hoffman; Jocelyn Zhao; Jianhua Yao; Le Lu; Lauren Kim; Evrim B Turkbey; Ronald M Summers
Journal:  Med Phys       Date:  2016-07       Impact factor: 4.071

4.  Snake model-based lymphoma segmentation for sequential CT images.

Authors:  Qiang Chen; Fang Quan; Jiajing Xu; Daniel L Rubin
Journal:  Comput Methods Programs Biomed       Date:  2013-06-17       Impact factor: 5.428

5.  Semi-automatic analysis of standard uptake values in serial PET/CT studies in patients with lung cancer and lymphoma.

Authors:  John Ly; Sabine Garpered; Peter Höglund; Eskil Jönsson; Sven Valind; Lars Edenbrandt; Per Wollmer
Journal:  BMC Med Imaging       Date:  2012-04-02       Impact factor: 1.930

  5 in total

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