Literature DB >> 15127747

Lymph node segmentation from CT images using fast marching method.

Jiayong Yan1, Tian-ge Zhuang, Binsheng Zhao, Lawrence H Schwartz.   

Abstract

Accurate lymph node size analysis is important medically. This paper presents an improved fast marching method to perform semi-automatic segmentation for lymph node from CT images. In this work, we have incorporated the gray scale information of the target region into the fast marching speed term and have given a hard constraint for the stop criteria, instead of only using the spatial image gradient, to remedy the 'boundary leaking' problem of the traditional fast marching method. Various experimental results are provided to demonstrate the effectiveness of the proposed method.

Mesh:

Year:  2004        PMID: 15127747     DOI: 10.1016/j.compmedimag.2003.09.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  9 in total

1.  Complete fully automatic model-based segmentation of normal and pathological lymph nodes in CT data.

Authors:  Lars Dornheim; Jana Dornheim; Ivo Rössling
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-10-08       Impact factor: 2.924

2.  Segmentation of the central-chest lymph nodes in 3D MDCT images.

Authors:  Kongkuo Lu; William E Higgins
Journal:  Comput Biol Med       Date:  2011-07-12       Impact factor: 4.589

Review 3.  Preoperative workflow for lymph nodes staging.

Authors:  Debora Botturi; Francesca Pizzorni Ferrarese; Giulia Angela Zamboni; Davide Zerbato
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

4.  Computer-aided lymph node segmentation in volumetric CT data.

Authors:  Reinhard R Beichel; Yao Wang
Journal:  Med Phys       Date:  2012-09       Impact factor: 4.071

5.  Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group.

Authors:  Sally F Barrington; N George Mikhaeel; Lale Kostakoglu; Michel Meignan; Martin Hutchings; Stefan P Müeller; Lawrence H Schwartz; Emanuele Zucca; Richard I Fisher; Judith Trotman; Otto S Hoekstra; Rodney J Hicks; Michael J O'Doherty; Roland Hustinx; Alberto Biggi; Bruce D Cheson
Journal:  J Clin Oncol       Date:  2014-09-20       Impact factor: 44.544

6.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

7.  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

8.  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

9.  Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study.

Authors:  Xingyu Zhao; Peiyi Xie; Mengmeng Wang; Wenru Li; Perry J Pickhardt; Wei Xia; Fei Xiong; Rui Zhang; Yao Xie; Junming Jian; Honglin Bai; Caifang Ni; Jinhui Gu; Tao Yu; Yuguo Tang; Xin Gao; Xiaochun Meng
Journal:  EBioMedicine       Date:  2020-06-05       Impact factor: 8.143

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.