Literature DB >> 18044586

Automated extraction of lymph nodes from 3-D abdominal CT images using 3-D minimum directional difference filter.

Takayuki Kitasaka1, Yukihiro Tsujimura, Yoshihiko Nakamura, Kensaku Mori, Yasuhito Suenaga, Masaaki Ito, Shigeru Nawano.   

Abstract

This paper presents a method for extracting lymph node regions from 3-D abdominal CT images using 3-D minimum directional difference filter. In the case of surgery of colonic cancer, resection of metastasis lesions is performed with resection of a primary lesion. Lymph nodes are main route of metastasis and are quite important for deciding resection area. Diagnosis of enlarged lymph nodes is quite important process for surgical planning. However, manual detection of enlarged lymph nodes on CT images is quite burden task. Thus, development of lymph node detection process is very helpful for assisting such surgical planning task. Although there are several report that present lymph node detection, these methods detect lymph nodes primary from PET images or detect in 2-D image processing way. There is no method that detects lymph nodes directly from 3-D images. The purpose of this paper is to show an automated method for detecting lymph nodes from 3-D abdominal CT images. This method employs a 3-D minimum directional difference filter for enhancing blob structures with suppressing line structures. After that, false positive regions caused by residua and vein are eliminated using several kinds of information such as size, blood vessels, air in the colon. We applied the proposed method to three cases of 3-D abdominal CT images. The experimental results showed that the proposed method could detect 57.0% of enlarged lymph nodes with 58 FPs per case.

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Year:  2007        PMID: 18044586     DOI: 10.1007/978-3-540-75759-7_41

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


  7 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

Review 2.  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

3.  2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.

Authors:  Ari Seff; Le Lu; Kevin M Cherry; Holger R Roth; Jiamin Liu; Shijun Wang; Joanne Hoffman; Evrim B Turkbey; Ronald M Summers
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

4.  Automated liver lesion detection in CT images based on multi-level geometric features.

Authors:  László Ruskó; Ádám Perényi
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-10-05       Impact factor: 2.924

5.  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

6.  Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks.

Authors:  Oscar A Debats; Geert J S Litjens; Henkjan J Huisman
Journal:  PeerJ       Date:  2019-11-22       Impact factor: 2.984

7.  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

  7 in total

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