Abdominal lymph node partitioning method based on attention mechanism neural network

A neural network and attention technology, applied in neural learning methods, biological neural network models, computer components, etc., can solve problems such as large differences in reading results and inaccurate prediction of abdominal lymph node divisions.

Active Publication Date: 2021-03-16
SICHUAN UNIV
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Problems solved by technology

[0005] Based on the above problems, the present invention provides an abdominal lymph node partition method based on the neural network of the attention mechanism, which is used to solve the p

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  • Abdominal lymph node partitioning method based on attention mechanism neural network
  • Abdominal lymph node partitioning method based on attention mechanism neural network
  • Abdominal lymph node partitioning method based on attention mechanism neural network

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Embodiment

[0062] Such as figure 1 As shown, an abdominal lymph node segmentation method based on the attention mechanism neural network includes the following steps:

[0063] Step 1: Data preparation, completing data import from the data system and calibration of abdominal lymph nodes to be classified;

[0064] Step 2: Mask generation, preprocessing the data, mainly including the preprocessing of the original CT image and using different strategies to generate the mask of the lymph node region;

[0065] Step 3: Build an attention mechanism residual network model, and use the collected data and calibration results to train the model;

[0066] Step 4: Repeat step 3 to build and train a model for the relative position and partition of abdominal lymph nodes; the "relative position" here refers to the position of abdominal lymph nodes in the abdominal structure relative to the complicated tissues such as organs and blood vessels, and the "partition" is defined by the location of lymph node...

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Abstract

The invention relates to the technical field of abdominal lymph node partitioning, in particular to an abdominal lymph node partitioning method based on an attention mechanism neural network. The abdominal lymph node partitioning method based on the attention mechanism neural network is used for solving the problems that in the prior art, a doctor has a large difference in film reading results ofthe same abdominal CT medical image, and prediction of abdominal lymph node partitioning is inaccurate. The method comprises the following steps: step 1, preparing data; step 2, generating a mask, andpreprocessing the data; step 3, constructing an attention mechanism residual network model; step 4, repeating the step 3, and constructing and training a model of lymph node relative position partitioning; and step 5, classifying the abdominal lymph nodes automatically detected by the detection task by using the model trained in the step 3 and the step 4. According to the method, the original CTimage and the mask are overlapped to serve as input, and the attention mechanism is introduced into the deep residual neural network, so that the abdominal lymph nodes in the CT image can be accurately partitioned.

Description

technical field [0001] The present invention relates to the technical field of abdominal lymph node division, and more specifically relates to an abdominal lymph node division method based on an attention mechanism neural network. Background technique [0002] In the prior art, lateral pelvic lymph node metastasis is one of the common forms of metastasis and recurrence of colorectal cancer in clinical practice. For T3 and T4 low rectal cancer below the peritoneal reflection, routine dissection of the lateral pelvic lymph nodes is required, and full abdominal enhanced CT Scanning is an important imaging method commonly used in clinic to judge the location and quality of the lateral lymph nodes of colorectal cancer, and it is one of the main ways for clinicians to identify whether the lateral lymph nodes have metastasized. No matter how difficult the task is, due to the complex distribution of abdominal organs, it is difficult to define the division of abdominal lymph nodes. W...

Claims

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Application Information

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IPC IPC(8): A61B6/03A61B6/00G06K9/62G06N3/08G06T7/00G06T7/11G06T7/13
CPCA61B6/032A61B6/50A61B6/5211G06T7/13G06T7/0012G06T7/11G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06F18/214
Inventor 王自强章毅黄昊王璟玲曾涵江张海仙孟文建王晗张许兵黄月瑶朱昱州潘震
Owner SICHUAN UNIV
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