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Liver tumor segmentation method and system based on deep learning

A liver tumor, deep learning technology, applied in the field of medical image processing and application, can solve the problems of timeliness, generality and poor accuracy

Pending Publication Date: 2021-05-04
JIANGNAN UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] For this reason, the technical problem to be solved by the present invention is to overcome the timeliness, versatility and poor accuracy of existing traditional segmentation methods when the tumors in different patients in CT images in the prior art have variable positions, shapes and sizes. In order to provide a time-effective, general-purpose and high-accuracy liver tumor segmentation method and system based on deep learning

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  • Liver tumor segmentation method and system based on deep learning
  • Liver tumor segmentation method and system based on deep learning
  • Liver tumor segmentation method and system based on deep learning

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Embodiment 1

[0023] Such as figure 1 and figure 2 As shown, this embodiment provides a liver tumor segmentation method based on deep learning, including the following steps: Step S1: Preprocessing the collected data set; Step S2: Building a network model based on the preprocessed data, wherein the The network model includes a plurality of lower convolutional layers and a plurality of upper convolutional layers, the lower convolutional layer is connected to the upper convolutional layer by skipping, and the features obtained by the lower convolutional layer are used in the skipping connection The graph is connected to the upper convolution through the attention module; step S3: input the preprocessed data into the network model for training to obtain the best network model; step S4: use the best network model to treat The processed CT images are segmented to obtain liver tumor regions.

[0024] In the liver tumor segmentation method based on deep learning described in this embodiment, in...

Embodiment 2

[0068] Based on the same inventive concept, this embodiment provides a liver tumor segmentation system based on deep learning, and its problem-solving principle is similar to that of the liver tumor segmentation method based on deep learning, and the repetition will not be repeated.

[0069] This embodiment provides a liver tumor segmentation system based on deep learning, including:

[0070] The preprocessing module is used to preprocess the collected data sets;

[0071] A building module for building a network model based on preprocessed data, wherein the network model includes a plurality of lower convolutional layers and a plurality of upper convolutional layers, and the lower convolutional layers are connected to the upper convolutional layers by skipping Layer, and the feature map obtained by the lower convolution layer is connected to the upper convolution through the attention module on the skip connection;

[0072] A training module, configured to input preprocessed ...

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Abstract

The invention relates to a liver tumor segmentation method and system based on deep learning. The method comprises the following steps: preprocessing a collected data set; a network model being built according to the preprocessed data, wherein the network model comprises a plurality of lower convolution layers and a plurality of upper convolution layers, the lower convolution layers are connected to the upper convolution layers through jumping, and feature maps obtained by the lower convolution layers are connected with the upper convolution layers through an attention module on jumping connection; inputting the preprocessed data into the network model for training to obtain an optimal network model; and segmenting a to-be-processed CT image by using the optimal network model to obtain a liver tumor region. According to the invention, wrong segmentation can be reduced, and high precision can be obtained.

Description

technical field [0001] The present invention relates to the technical field of medical image processing and application, in particular to a liver tumor segmentation method and system based on deep learning. Background technique [0002] In recent years, with the development of today's society, CT / MRI imaging has been widely used in the field of medical imaging. Doctors often need to manually segment the lesion area in CT / MRI images before making a diagnosis, so as to provide assistance for subsequent surgical planning and tumor treatment evaluation. However, this manual segmentation method is not only time-consuming and labor-intensive, but also susceptible to segmentation differences caused by the subjective judgment of doctors. Therefore, the use of computer algorithms to realize the automatic segmentation of liver tumors in CT / MRI images can greatly reduce the workload of doctors, and at the same time improve the accuracy and repeatability of liver tumor segmentation ser...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/084G06T2207/30056G06T2207/30096G06T2207/10081G06T2207/20081G06T2207/20084G06N3/047
Inventor 肖志勇刘一鸣柴志雷周锋盛丁炎张雨
Owner JIANGNAN UNIV
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