Coarse-to-fine cascade depth network based full-automatic left ventricle segmentation method

A deep network, automatic segmentation technology, applied in the field of artificial intelligence medical imaging analysis, can solve the problems of complex models, lack of high efficiency and robustness, and achieve the effect of improving robustness, enhancing training data, and improving accuracy

Active Publication Date: 2018-01-12
WUHAN UNIV
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Problems solved by technology

[0013] The problems faced by fully automatic segmentation of the left ventricle are complex and the model lacks efficient and robust challenges

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  • Coarse-to-fine cascade depth network based full-automatic left ventricle segmentation method
  • Coarse-to-fine cascade depth network based full-automatic left ventricle segmentation method
  • Coarse-to-fine cascade depth network based full-automatic left ventricle segmentation method

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Embodiment Construction

[0024] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0025] please see figure 1, a method for fully automatic segmentation of the left ventricle of the heart from coarse to fine cascaded deep network provided by the present invention, comprising the following steps:

[0026] Step 1: Step 1: Based on the original input image I 0 and the original images manually annotated by medical imaging experts I 0 The pixel point coordinate set S of the upper left ventricle contour is used to construct the region of interest ROI detection training set A for left ventricle segmentation 0 and the left ventricle segmentation...

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Abstract

The invention discloses a coarse-to-fine cascade depth network based full-automatic left ventricle segmentation method. The method includes: firstly, subjecting training data to preprocessing and dataenhancement to obtain sufficient training data; secondly, adopting a depth network for detecting a left ventricle included ROI (region of interest), and realizing left ventricle fine segmentation inthe left ventricle ROI by the depth network; finally, mapping left ventricle segmented from the left ventricle ROI into an original input image. In order to improve segmentation performances, the cascade depth network (CasNet) is provided for left ventricle ROI detection and left ventricle fine segmentation, and the cascade network improves left ventricle segmentation accuracy through hidden enhanced training data and step-by-step refining of segmentation results. In addition, each network unit for the cascade network is simple, the defect of influences on efficiency by numerous parameters incomplex networks is avoided, and high efficiency of left ventricle segmentation is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence medical imaging analysis, and relates to a fully automatic segmentation method of the left ventricle of the heart, in particular to a deep cascade network for left ventricle segmentation and a fully automatic segmentation method from coarse to fine. Background technique [0002] According to the statistical report of the World Health Organization (WHO, World Health Organization), cardiovascular diseases (CVDs, Cardiovascular Diseases) are the leading cause of global population death (Document 1). Rapid and intelligent diagnosis of cardiovascular diseases is crucial for personalized surgical planning and intraoperative navigation (Reference 2). Rapid and accurate segmentation of the left ventricle is a key step in calculating clinical indicators (such as ventricular volume, ejection fraction, etc.) and then diagnosing and treating cardiovascular diseases (Reference 3). However, the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/12G06N3/04
Inventor 袁志勇童倩倩袁田琛李潇洒刘之兵
Owner WUHAN UNIV
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