WO2013007063A1 - Procédé destiné à l'identification d'une scissure des poumons sur une image ct de poumons - Google Patents

Procédé destiné à l'identification d'une scissure des poumons sur une image ct de poumons Download PDF

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Publication number
WO2013007063A1
WO2013007063A1 PCT/CN2011/079226 CN2011079226W WO2013007063A1 WO 2013007063 A1 WO2013007063 A1 WO 2013007063A1 CN 2011079226 W CN2011079226 W CN 2011079226W WO 2013007063 A1 WO2013007063 A1 WO 2013007063A1
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WO
WIPO (PCT)
Prior art keywords
lung
plane
fissure
lung fissure
image
Prior art date
Application number
PCT/CN2011/079226
Other languages
English (en)
Chinese (zh)
Inventor
普建涛
孟鑫
Original Assignee
Pu Jiantao
Meng Xin
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pu Jiantao, Meng Xin filed Critical Pu Jiantao
Publication of WO2013007063A1 publication Critical patent/WO2013007063A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung

Definitions

  • this method is not sensitive to noise or outliers.
  • FIGS. 18 through 20 show lung fissure recognition in an abnormality test with severe bronchiectasis (cystic fibrosis).
  • 21 to 23 are examples in which it is difficult to identify a lung fissure by the new invention method. It is very difficult to clearly see the location of the lungs. Applying the previous method [1] to this test is completely invalid.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

L'invention a trait à un procédé de détection et de segmentation automatiques destiné à l'identification efficace et précise d'une scissure des poumons sur une image CT grâce à une adaptation de plan en trois dimensions. Selon ce procédé, une image CT est considérée comme un groupe de nuages de points spatiaux en trois dimensions, une zone des poumons est divisée en petits corps sphériques subdivisés, puis un procédé d'adaptation de plan en trois dimensions est utilisé pour rechercher un plan de scissure des poumons dans ces petits corps sphériques subdivisés, de manière à convertir la détection de scissure des poumons ayant des caractéristiques de surface courbe libre en détection de plan, ce qui réduit bien évidemment la complexité du problème. Ce procédé présente l'avantage d'être insensible au bruit ainsi qu'aux valeurs anormales. De plus, au cours du processus de détection d'une scissure des poumons, l'identification des scissures de différents types (autrement dit d'une scissure oblique et d'une scissure horizontale sur les poumons gauche et droit) est achevée grâce à un simple procédé de groupement. Par rapport aux autres procédés, le procédé de la présente invention est très précis, stable et extrêmement efficace.
PCT/CN2011/079226 2011-07-08 2011-09-01 Procédé destiné à l'identification d'une scissure des poumons sur une image ct de poumons WO2013007063A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201110190797.2 2011-07-08
CN2011101907972A CN102254097A (zh) 2011-07-08 2011-07-08 肺部ct图像上的肺裂识别方法

Publications (1)

Publication Number Publication Date
WO2013007063A1 true WO2013007063A1 (fr) 2013-01-17

Family

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PCT/CN2011/079226 WO2013007063A1 (fr) 2011-07-08 2011-09-01 Procédé destiné à l'identification d'une scissure des poumons sur une image ct de poumons

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Country Link
CN (1) CN102254097A (fr)
WO (1) WO2013007063A1 (fr)

Cited By (2)

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EP3968271A1 (fr) 2020-09-11 2022-03-16 Bayer AG Analyse des branches intrapulmonaires
US11295451B2 (en) 2016-08-02 2022-04-05 Koninklijke Philips N.V. Robust pulmonary lobe segmentation

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CN102629327A (zh) * 2011-12-02 2012-08-08 普建涛 气道壁识别方法
AU2015284303B2 (en) * 2014-07-02 2019-07-25 Covidien Lp System and method for detecting trachea
CN104574319B (zh) * 2015-01-22 2017-10-13 深圳大学 一种肺部ct图像的血管增强方法及系统
CN106373118B (zh) * 2016-08-30 2017-09-22 华中科技大学 可有效保留边界和局部特征的复杂曲面零件点云精简方法
CN106875379A (zh) * 2017-01-10 2017-06-20 陕西渭南神州德信医学成像技术有限公司 肺裂完整度评估方法、装置和系统
WO2019000455A1 (fr) 2017-06-30 2019-01-03 上海联影医疗科技有限公司 Procédé et système de segmentation d'image
CN107392910B (zh) * 2017-07-06 2020-01-07 东软医疗系统股份有限公司 一种基于ct图像的肺叶分割方法及装置
CN111709953B (zh) * 2017-11-03 2023-04-07 杭州依图医疗技术有限公司 Ct影像的肺叶段分割中的输出方法、装置
CN109300122B (zh) * 2018-09-14 2023-04-07 东软医疗系统股份有限公司 图像处理与阈值确定方法、装置及设备
CN109658425B (zh) * 2018-12-12 2021-12-28 上海联影医疗科技股份有限公司 一种肺叶分割方法、装置、计算机设备及存储介质
CN111986299B (zh) * 2019-05-24 2024-03-01 北京京东乾石科技有限公司 点云数据处理方法、装置、设备及存储介质
CN113160186B (zh) * 2021-04-27 2022-10-25 青岛海信医疗设备股份有限公司 一种肺叶分割方法及相关装置
CN114092470B (zh) * 2021-12-08 2022-08-09 之江实验室 一种基于深度学习的肺裂自动检测方法及装置

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11295451B2 (en) 2016-08-02 2022-04-05 Koninklijke Philips N.V. Robust pulmonary lobe segmentation
EP3968271A1 (fr) 2020-09-11 2022-03-16 Bayer AG Analyse des branches intrapulmonaires

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