Literature DB >> 17027116

Semi-automated CT segmentation using optic flow and Fourier interpolation techniques.

Tzung-Chi Huang1, Geoffrey Zhang, Thomas Guerrero, George Starkschall, Kan-Ping Lin, Ken Forster.   

Abstract

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.

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Year:  2006        PMID: 17027116     DOI: 10.1016/j.cmpb.2006.09.003

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  9 in total

1.  Comparison of intensity-modulated radiotherapy planning based on manual and automatically generated contours using deformable image registration in four-dimensional computed tomography of lung cancer patients.

Authors:  Elisabeth Weiss; Krishni Wijesooriya; Viswanathan Ramakrishnan; Paul J Keall
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-12-19       Impact factor: 7.038

2.  CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Authors:  Shuai Wang; Kelei He; Dong Nie; Sihang Zhou; Yaozong Gao; Dinggang Shen
Journal:  Med Image Anal       Date:  2019-03-21       Impact factor: 8.545

3.  Self-assessed performance improves statistical fusion of image labels.

Authors:  Frederick W Bryan; Zhoubing Xu; Andrew J Asman; Wade M Allen; Daniel S Reich; Bennett A Landman
Journal:  Med Phys       Date:  2014-03       Impact factor: 4.071

4.  Principal curve based semi-automatic segmentation of organs in 3D-CT.

Authors:  S You; E Bas; E Ataer-Cansizoglu; J Kalpathy-Cramer; Deniz Erdogmus
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

5.  Fully automated esophagus segmentation with a hierarchical deep learning approach.

Authors:  Roger Trullo; Caroline Petitjean; Dong Nie; Dinggang Shen; Su Ruan
Journal:  Conf Proc IEEE Int Conf Signal Image Process Appl       Date:  2017-12-01

6.  Deciphering tumour tissue organization by 3D electron microscopy and machine learning.

Authors:  Baudouin Denis de Senneville; Fatma Zohra Khoubai; Marc Bevilacqua; Alexandre Labedade; Kathleen Flosseau; Christophe Chardot; Sophie Branchereau; Jean Ripoche; Stefano Cairo; Etienne Gontier; Christophe F Grosset
Journal:  Commun Biol       Date:  2021-12-13

7.  Esophagus segmentation from 3D CT data using skeleton prior-based graph cut.

Authors:  Damien Grosgeorge; Caroline Petitjean; Bernard Dubray; Su Ruan
Journal:  Comput Math Methods Med       Date:  2013-08-29       Impact factor: 2.238

8.  Determination of an optimal organ set to implement deformations to support four-dimensional dose calculations in radiation therapy planning.

Authors:  Wafa Soofi; George Starkschall; Keith Britton; Sastry Vedam
Journal:  J Appl Clin Med Phys       Date:  2008-04-28       Impact factor: 2.102

9.  Use of three-dimensional (3D) optical flow method in mapping 3D anatomic structure and tumor contours across four-dimensional computed tomography data.

Authors:  Geoffrey Zhang; Tzung-Chi Huang; Thomas Guerrero; Kang-Ping Lin; Craig Stevens; George Starkschall; Ken Forster
Journal:  J Appl Clin Med Phys       Date:  2008-02-05       Impact factor: 2.102

  9 in total

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