Literature DB >> 16685876

Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate.

B C Davis1, M Foskey, J Rosenman, L Goyal, S Chang, S Joshi.   

Abstract

We have been developing an approach for automatically quantifying organ motion for adaptive radiation therapy of the prostate. Our approach is based on deformable image registration, which makes it possible to establish a correspondence between points in images taken on different days. This correspondence can be used to study organ motion and to accumulate inter-fraction dose. In prostate images, however, the presence of bowel gas can cause significant correspondence errors. To account for this problem, we have developed a novel method that combines large deformation image registration with a bowel gas segmentation and deflation algorithm. In this paper, we describe our approach and present a study of its accuracy for adaptive radiation therapy of the prostate. All experiments are carried out on 3-dimensional CT images.

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Year:  2005        PMID: 16685876     DOI: 10.1007/11566465_55

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  24 in total

1.  A Learning based Hierarchical Framework for Automatic Prostate Localization in CT Images.

Authors:  Shu Liao; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

2.  Learning image context for segmentation of prostate in CT-guided radiotherapy.

Authors:  Wei Li; Shu Liao; Qianjin Feng; Wufan Chen; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

3.  Segmenting CT prostate images using population and patient-specific statistics for radiotherapy.

Authors:  Qianjin Feng; Mark Foskey; Wufan Chen; Dinggang Shen
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

4.  Objective assessment of deformable image registration in radiotherapy: a multi-institution study.

Authors:  Rojano Kashani; Martina Hub; James M Balter; Marc L Kessler; Lei Dong; Lifei Zhang; Lei Xing; Yaoqin Xie; David Hawkes; Julia A Schnabel; Jamie McClelland; Sarang Joshi; Quan Chen; Weiguo Lu
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

5.  Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy.

Authors:  Xiaofeng Yang; Peter Rossi; Tomi Ogunleye; David M Marcus; Ashesh B Jani; Hui Mao; Walter J Curran; Tian Liu
Journal:  Med Phys       Date:  2014-11       Impact factor: 4.071

6.  Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

Authors:  Sang Hyun Park; Yaozong Gao; Yinghuan Shi; Dinggang Shen
Journal:  Med Phys       Date:  2014-11       Impact factor: 4.071

7.  Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models.

Authors:  Dengwang Li; Pengxiao Zang; Xiangfei Chai; Yi Cui; Ruijiang Li; Lei Xing
Journal:  Med Phys       Date:  2016-10       Impact factor: 4.071

8.  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

9.  Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images.

Authors:  Xiubin Dai; Yaozong Gao; Dinggang Shen
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

10.  A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression.

Authors:  Yinghuan Shi; Yaozong Gao; Shu Liao; Daoqiang Zhang; Yang Gao; Dinggang Shen
Journal:  Neurocomputing       Date:  2016-01-15       Impact factor: 5.719

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