A Neural Network-Based Segmentation Method and System for Hepatic Hydatid Lesions

A neural network and convolutional neural network technology, applied in the field of liver hydatid detection, can solve problems such as poor results, reduce missed diagnosis, and improve diagnostic efficiency and accuracy.

Active Publication Date: 2020-11-17
TSINGHUA UNIV
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Problems solved by technology

Patients with alveolar echinococcosis do not respond well to oral medication, and must undergo surgical resection to achieve a radical cure

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  • A Neural Network-Based Segmentation Method and System for Hepatic Hydatid Lesions
  • A Neural Network-Based Segmentation Method and System for Hepatic Hydatid Lesions
  • A Neural Network-Based Segmentation Method and System for Hepatic Hydatid Lesions

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Embodiment Construction

[0050] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0051] Such as figure 1 As shown, a neural network-based segmentation method for liver hydatid lesions, including:

[0052] S1. Obtain the segmented liver region from the cystic hydatid CT image set through the liver segmentation model, train and verify the cystic hydatid lesion segmentation model based on the liver segmentation results, and mark whether the lesion is active during training and verification;

[0053] S2. Obtain the segmented liver region from the CT image set of alveolar echinococcosis through the liver segmentation model, identify and segment the blood vessels of the acquired liver region, train and verify the segmentation of alveolar echinococcosis lesion based on the results of blood vessel segmentation and liver segmentation Model, during training and verification, mark whether the lesion invades blood vessels an...

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Abstract

The invention discloses a neural network-based method and system for segmenting hepatic echinococcosis lesions. The method includes: S1, training and verifying the segmentation model of cystic echinococcosis lesions; S2, training and verifying the segmentation model of alveolar echinococcosis lesions; S3, Obtain the segmented liver area from a hydatid CT image, input the liver area into the lesion recognition model, and obtain the recognition result; S4. When the recognition result is determined to be a cystic hydatid lesion, input the VOI area into the cystic hydatid lesion segmentation model to obtain the first segmentation result; S5. When it is determined that the recognition result is an alveolar echinococcosis lesion, perform blood vessel identification and segmentation on the VOI area, and input the vessel segmentation result and the VOI area into the alveolar echinococcosis lesion segmentation model to obtain the second Split results. The method and system provided by the present invention perform fusion recognition and feature extraction on multi-modal medical images through various models, assist doctors in screening echinococcosis, and improve diagnostic efficiency and accuracy.

Description

technical field [0001] The invention relates to the technical field of liver echinococcosis detection, in particular to a method and system for segmenting hepatic echinococcosis lesions based on a neural network. Background technique [0002] Hydatid disease is a serious zoonotic parasitic disease that spreads across all continents of the world. The number of people and patients threatened by echinococcosis in my country ranks first in the world, and the infection rate of hermaphrodite echinococcosis in the hardest-hit Sanjiangyuan area of ​​Qinghai Province is 8.93-12.38%. The environment in this area is harsh, medical resources are scarce, and the level of doctors is not homogeneous. Echinococcosis is mainly divided into cystic echinococcosis and alveolar echinococcosis. The impact of cystic echinococcosis on the host is mainly manifested in the damage to the structure and function of parasitic tissues and organs. During its expansive growth, it produces compression sympt...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/11G06T7/136G06N3/04G06T5/30A61B6/00A61B6/03
CPCG06T5/30G06T7/11G06T7/136A61B6/032A61B6/5205A61B6/5211G06T2207/20081G06T2207/20084G06T2207/10081G06T2207/30101G06T2207/30056G06N3/045
Inventor 王展沈新科胥瑾辛盛海樊海宁王海久周瀛任利阳丹才让马洁王志鑫
Owner TSINGHUA UNIV
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