WO2014094376A1 - Procédé de segmentation automatique d'image de cancer du col de l'utérus à partir d'irm-t2 et d'irm pondérée en diffusion - Google Patents

Procédé de segmentation automatique d'image de cancer du col de l'utérus à partir d'irm-t2 et d'irm pondérée en diffusion Download PDF

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WO2014094376A1
WO2014094376A1 PCT/CN2013/070942 CN2013070942W WO2014094376A1 WO 2014094376 A1 WO2014094376 A1 WO 2014094376A1 CN 2013070942 W CN2013070942 W CN 2013070942W WO 2014094376 A1 WO2014094376 A1 WO 2014094376A1
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image
segmentation
tumor
mri
bladder
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PCT/CN2013/070942
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English (en)
Chinese (zh)
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李悟
考月英
田捷
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中国科学院自动化研究所
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Publication of WO2014094376A1 publication Critical patent/WO2014094376A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/143—Segmentation; Edge detection involving probabilistic approaches, e.g. Markov random field [MRF] modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • 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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • 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

Definitions

  • the invention belongs to the field of image processing, and particularly relates to a method for automatically segmenting cervical cancer images based on T2-weighted nuclear magnetic resonance imaging T2-MRI and diffusion weighted magnetic resonance imaging DW-MRI. Background technique
  • Cervical cancer is one of the common malignant tumors that seriously threaten women's life and health.
  • the accurate segmentation of cervical cancer has important clinical significance and application value for the prevention, diagnosis and treatment of cervical cancer.
  • FIG. 1 (a) and (b) show the T2-MR image and DW-MR image of the abdomen of cervical cancer patients.
  • the cervix is located between the bladder and the rectum. It can be seen from Fig.
  • CMAP combined maximum posterior probability
  • a T2-weighted magnetic resonance imaging T2-MRI and diffusion-weighted magnetic resonance imaging DW-MRI automatic segmentation method for cervical cancer images including:
  • Step 1 Register the DW-MR image into the T2-MR image by nonlinear registration method, and classify the registered DW-MR image;
  • Step 2 Filter the T2-MR image using nonlinear anisotropic diffusion filtering technique, segment the bladder and rectum, and segment the region of interest using the segmentation results of the bladder and rectum;
  • Step 3 For T2-MR images The region of interest and the DW-MR image were combined with the maximum posterior probability CMAP for precise segmentation of the tumor.
  • the invention fully utilizes the effective information of the T2-MR image and the DW-MR image, and can effectively overcome the influence of noise, local volume effect and intensity overlap in the T2-MR image, and is an accurate and effective segmentation method for cervical cancer. It has important clinical significance and application value for the prevention, diagnosis and treatment of cervical cancer.
  • Figure 1 is an anatomical view of a patient with cervical cancer, (a) is a T2-M image; (b) is a DW-MR image;
  • Figure 2 is an automatic segmentation frame diagram based on T2-MRI and DW-MRI
  • FIG. 3 is a flow chart of the Maximum Posterior Probability Method (CMAP);
  • the core idea of the present invention is a T2-weighted magnetic resonance imaging (T2-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI) automatic segmentation framework for cervical cancer images and the use of joint maximum posterior probability (CMAP).
  • the method for segmenting the cervical cancer tumor region includes the following steps: First, the DW-MR image is registered to the T2-MR image by using a nonlinear registration method (here, the mutual information registration method is taken as an example), and the registration is performed.
  • DW-MR images are classified; then T2-MR images are filtered using nonlinear anisotropic diffusion filtering techniques (here, PM nonlinear anisotropic diffusion filtering is used as an example), then the bladder and rectum are segmented, and the bladder and The segmentation result of the rectum divides the region of interest (including normal tissues and tumors). Finally, the region of interest and the DW-MR image of the T2-MR image are combined with the maximum posterior probability (CMAP) for accurate segmentation of the tumor.
  • CMAP maximum posterior probability
  • FIG. 2 is a flowchart of a T2 weighted magnetic resonance imaging (T2-MRI) and diffusion weighted magnetic resonance imaging (DW-MRI) cervical segmentation image automatic segmentation framework provided by the present invention, which comprises the following steps. :
  • Step 1 Use a nonlinear registration method (such as mutual information registration method, Demons algorithm, etc.) to register the DW-MR image to the T2-MR image, and sub-step the registered DW-MR image.
  • Step 2 Adopt The nonlinear anisotropic diffusion filtering technique filters the T2-MR image, then segments the bladder and rectum, and uses the segmentation results of the bladder and rectum to segment the region of interest (including normal tissues and tumors);
  • Step 3 Accurate segmentation of the tumor is performed using the combined maximum posterior probability (CMAP) method for the region of interest and the DW-MR image of the T2-M image.
  • Step 1 above includes the following two small steps: 1) Registering the DW-MR image to the T2-MR image using a nonlinear registration method, where the mutual information registration method is used as an example; 2) The registered DW-MR The image classification is an automatic threshold classification method to achieve the initial segmentation and localization of the tumor.
  • CMAP maximum posterior probability
  • the above step 2 includes the following four steps: 1) filtering the T2-M image by using a nonlinear anisotropic diffusion filtering technique to preserve the edge information while removing noise, where PM nonlinear anisotropic diffusion filtering is taken as an example; Bladder segmentation; 3) rectal segmentation; 4) segmentation of the region of interest.
  • the gradient threshold is the gradient threshold.
  • the method used for bladder segmentation in the second small step above is the active contour model.
  • the model was built based on our prior knowledge that the bladder is the more uniform monolithic region with the highest gray value in the T2-M image of the abdominal cavity.
  • the rectal segmentation uses the a priori knowledge that the rectum is located below the cervix and the preliminary segmentation results of the tumor in step 1.
  • the fuzzy C-means algorithm is used on the T2-MR image of the bladder to obtain the rectal segmentation. result.
  • the region of interest is segmented.
  • the preliminary segmentation result of the tumor in step 1 is used, and the fuzzy C-means algorithm is used to segment the inclusion.
  • the region of interest of the tumor and normal tissue, the results are shown in Figure 4 (b).
  • step 3 is to accurately segment the tumor by using the combined maximum posterior probability (CMAP) method for the region of interest and the DW-MR image of the T2-MR image, and the result is shown in Fig. 4(e).
  • CMAP maximum posterior probability
  • CMAP joint maximum a posteriori probability
  • the specific steps are as follows: 1) Calculate the energy function of the T2-M image ⁇ 2 ; 2) Calculate the energy function of the DW-M image t/ (x 3) Calculate the joint energy function of the T2-MR image and the DW-MR image ⁇ 2 ( + ⁇ / ( ; 4) to determine whether the termination condition is satisfied, and if so, determine the type of tumor and normal tissue according to the principle of minimum energy, thereby Output the accurately segmented tumor area; return to step 1 if the termination condition is not met.
  • the corresponding calculation formula in each small step is shown in the following description.
  • the traditional MAP segmentation algorithm is to obtain the segmentation result L ⁇ , which makes the posterior probability ⁇ x
  • X arg max (
  • ⁇ ⁇ is the gray mean of the first type of tissue at the pixel
  • n ik is the Gaussian white noise corresponding to the tissue at the pixel Z
  • x) It can be expressed as:
  • t ⁇ x ⁇ is a potential function
  • ⁇ is a constant
  • the energy function f/ 2 (x) of the T2-M image in the first small step described above can be calculated from the calculation formula of f/(x).
  • the energy function ⁇ / ( ⁇ ) of the DW-MR image in the second small step above can be calculated according to the calculation formula of [/(X).
  • ? is the weight coefficient, which is used to balance the influence of the T2-MR image and the DW-MR image on the segmentation result.
  • FIG. 4 shows the effect of the T2-weighted magnetic resonance imaging (T2-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI) cervical cancer image segmentation framework experiments.
  • T2-MRI T2-weighted magnetic resonance imaging
  • DW-MRI diffusion-weighted magnetic resonance imaging
  • the method of the present invention is based on T2-weighted magnetic resonance imaging (T2-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI) automatic segmentation framework for cervical cancer images and the use of joint maximum posterior probability (CMAP).
  • T2-MRI T2-weighted magnetic resonance imaging
  • DW-MRI diffusion-weighted magnetic resonance imaging
  • CMAP joint maximum posterior probability

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

La présente invention concerne un procédé de segmentation automatique d'une image de cancer du col de l'utérus basée sur une imagerie à résonance magnétique en pondération T2 (IRM-T2) et sur une imagerie à résonance magnétique pondérée en diffusion (IRM-DW), consistant à : enregistrer une image par IRM-DW sur une image par IRM-T2 au moyen d'un procédé d'enregistrement non linéaire, et classifier l'image par IRM-DW enregistrée ; filtrer l'image par IRM-T2 au moyen d'une technique de filtrage par diffusion anisotrope non-linéaire, segmenter une vessie et un rectum, et segmenter une région d'intérêt en utilisant les résultats de segmentation de la vessie et du rectum ; et effectuer une segmentation précise d'une tumeur sur la région d'intérêt de l'image par IRM-T2 et de l'image par IRM-DW en utilisant un procédé combiné de probabilité maximale à posteriori (CMAP). La présente invention utilise pleinement les informations effectives concernant une image par IRM-T2 et une image par IRM-DW, permet de surpasser efficacement les influences du bruit, de l'effet de volume partiel et du chevauchement d'intensité dans l'image par IRM-T2, et constitue un procédé de segmentation précis et efficace pour le cancer du col de l'utérus, et représente une signification clinique et une valeur d'application importantes en termes de prévention, de diagnostic et de traitement du cancer du col de l'utérus.
PCT/CN2013/070942 2012-12-19 2013-01-24 Procédé de segmentation automatique d'image de cancer du col de l'utérus à partir d'irm-t2 et d'irm pondérée en diffusion WO2014094376A1 (fr)

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RU2613083C1 (ru) * 2015-12-28 2017-03-15 Федеральное государственное бюджетное образовательное учреждение высшего образования "Российский национальный исследовательский медицинский университет имени Н.И. Пирогова" Министерства здравоохранения Российской Федерации (ФГБОУ ВО РНИМУ им. Н.И. Пирогова Минздрава России) Способ определения объема опухоли при раке шейки матки при проведении магнитно-резонансной томографии
RU2657845C1 (ru) * 2017-06-28 2018-06-15 Федеральное государственное бюджетное образовательное учреждение дополнительного профессионального образования "Российская медицинская академия непрерывного профессионального образования" Министерства здравоохранения Российской Федерации (ФГБОУ ДПО РМАНПО Минздрава России) Способ диагностики спондилогенной шейной миелопатии без компрессии спинного мозга
US11049221B2 (en) 2018-04-27 2021-06-29 Imam Abdulrahman Bin Faisal University Gradient vector orientation based nonlinear diffusion filter
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CN117333725A (zh) * 2023-11-29 2024-01-02 中国医学科学院北京协和医院 一种基于mri的先天性宫颈畸形分类方法、系统及设备

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