Automated incision line determination for virtual unfolded view generation of the stomach from 3D abdominal CT images

被引:0
|
作者
Suito, Tomoaki [1 ]
Oda, Masahiro [1 ]
Kitasaka, Takayuki [2 ]
Iinuma, Gen [3 ]
Misawa, Kazunari [4 ]
Nawano, Shigeru [5 ]
Mori, Kensaku [1 ,6 ]
机构
[1] Nagoya Univ, Grad Sch Informat Sci, Chikusa Ku, Nagoya, Aichi 4648603, Japan
[2] Aichi Inst Technol Japan, Sch Informat Sci, Toyota, Aichi 4700392, Japan
[3] Natl Canc Ctr, Tokyo 1040045, Japan
[4] Aichi Canc Ctr Hosp, Nagoya, Aichi 4648681, Japan
[5] Int Univ Hlth & Welfare, Mita Hosp, Minato Ku, Tokyo 1088329, Japan
[6] Nagoya Univ, Strategy Off Informat & Communicat Headquarters, Chikusa Ku, Nagoya, Aichi 4648601, Japan
来源
MEDICAL IMAGING 2012: COMPUTER-AIDED DIAGNOSIS | 2012年 / 8315卷
基金
日本学术振兴会;
关键词
CT image; virtual unfolded view; virtual endoscopy; incision line;
D O I
10.1117/12.911409
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In this paper, we propose an automated incision line determination method for virtual unfolded view generation of the stomach from 3D abdominal CT images. The previous virtual unfolding methods of the stomach required a lot of manual operations such as determination of the incision line, which heavily tasks an operator. In general, an incision line along the greater curvature of the stomach is used for making pathological specimen. In our method, an incision line is automatically determined by projecting a centerline of the stomach onto the gastric surface from a projection line. The projection line is determined by using positions of the cardia and the pylorus, that can be easily specified by two mouse clicks. The process of our method is performed as follows. We extract the stomach region using a thresholding and a labeling processes. We apply a thinning process to the stomach region, and then we extract the longest line from the result of the thinning process. We obtain a centerline of the stomach region by smoothing the longest line by using a Bezier curve. The incision line is calculated by projecting the centerline onto the gastric surface from the projection line. We applied the proposed method to 19 cases of CT images. We automatically determined incision lines. Experimintal results showed our method was able to determine incision lines along the greater curvature for most of 19 cases.
引用
收藏
页数:7
相关论文
共 9 条
  • [1] Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes
    Oda, Masahiro
    Suito, Tomoaki
    Hayashi, Yuichiro
    Kitasaka, Takayuki
    Furukawa, Kazuhiro
    Miyahara, Ryoji
    Hirooka, Yoshiki
    Goto, Hidemi
    Iinuma, Gen
    Misawa, Kazunari
    Nawano, Shigeru
    Mori, Kensaku
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI 2013), PT I, 2013, 8149 : 332 - 339
  • [2] Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images
    Oda, Masahiro
    Hoang, Bui Huy
    Kitasaka, Takayuki
    Misawa, Kazunari
    Fujiwara, Michitaka
    Mori, Kensaku
    MEDICAL IMAGING 2012: IMAGE PROCESSING, 2012, 8314
  • [3] A method for automated anatomical labeling of abdominal veins extracted from 3D CT images
    Matsuzaki, Tetsuro
    Oda, Masahiro
    Kitasaka, Takayuki
    Hayashi, Yuichiro
    Misawa, Kazunari
    Mori, Kensaku
    MEDICAL IMAGING 2013: IMAGE PROCESSING, 2013, 8669
  • [4] A study on automated anatomical labeling to arteries concerning with colon from 3D abdominal CT images
    Hoang, Bui Huy
    Oda, Masahiro
    Jiang, Zhengang
    Kitasaka, Takayuki
    Misawa, Kazunari
    Fujiwara, Michitaka
    Mori, Kensaku
    MEDICAL IMAGING 2011: IMAGE PROCESSING, 2011, 7962
  • [5] 3D reconstruction of bone model from the medical CT images
    Bao Zhongxian
    Yin Yan
    Guo Aijun
    ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION, 2006, : 538 - 540
  • [6] 3D Shape Reconstruction of Puppet Head from CT Images by Machine Learning
    Ikeda, Hinata
    Ukida, Hiroyuki
    Yamazoe, Kouki
    Tominaga, Masahide
    Sasao, Tomoyo
    Terada, Kenji
    2022 61ST ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE), 2022, : 592 - 597
  • [7] 3D visualisation of the middle ear and adjacent structures using reconstructed multi-slice CT datasets, correlating 3D images and virtual endoscopy to the 2D cross-sectional images
    Rodt, T
    Ratiu, P
    Becker, H
    Bartling, S
    Kacher, DF
    Anderson, M
    Jolesz, FA
    Kikinis, R
    NEURORADIOLOGY, 2002, 44 (09) : 783 - 790
  • [8] Fully Automated CAD System for Lung Cancer Detection and Classification Using 3D Residual U-Net with multi-Region Proposal Network (mRPN) in CT Images
    Masood, Anum
    Naseem, Usman
    Nasim, Mehwish
    CANCER PREVENTION THROUGH EARLY DETECTION, CAPTION 2023, 2023, 14295 : 29 - 39
  • [9] A procedure for automated assignment of anatomical names of bronchial branches extracted from 3-D X-ray CT images and its application to virtualized endoscope system
    Mori, K
    Toriwaki, J
    Hasegawa, J
    Anno, H
    Katada, K
    COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING, 1999, 1182 : 107 - 112