Automatic delineation method of mediastinal lymphatic drainage area based on deep learning network

Abstract

The invention discloses an automatic delineation method of the mediastinal lymphatic drainage area based on a deep learning network, which is suitable for CT images. The automatic delineation method includes the following steps: S1: collecting CT image data and images of the mediastinal lymphatic drainage area manually marked by doctors, And preprocess the CT image data and the images of the mediastinal lymphatic drainage area manually marked by doctors; S2: group the preprocessed CT image data to obtain the training set, verification set and test set; S3: pair the training set, verification set and test set Step S4: Build a deep learning segmentation model; and S5: Input the CT image data in the training set and the images of the mediastinal lymphatic drainage area manually marked by doctors into the deep learning segmentation model that has been constructed. After the training iterations converge, save The segmentation model of the mediastinal lymphatic drainage area, and then identify and predict the mediastinal lymphatic drainage area, and obtain the probability map of each partition of the mediastinal lymphatic drainage area. The network can better locate and segment small drainage areas.

Description

technical field [0001] The invention relates to the field of medical images, in particular to an automatic delineation method of a mediastinal lymphatic drainage area based on a deep learning network. Background technique [0002] In the field of radiotherapy, precise tumor radiotherapy technology can effectively improve the curative effect of patients and reduce toxic and side effects, while precise radiotherapy relies on precise target contours. In the process of delineating the target area, it is necessary to carefully delineate the target area with reference to the drainage range of the drainage area. In addition, the mediastinal lymphatic drainage area also plays a very important role in the formulation of clinical staging and treatment principles for patients with lung cancer. Therefore, the automatic delineation of the drainage area has very important clinical significance. This method helps clinicians to delineate the mediastinal drainage area quickly, accurately a...

AI Technical Summary

Problems solved by technology

[0004] First, the sketching speed is slow, which consumes a lot of precious time for doctors; second, the accuracy of sketching depends on the doctor's clinical experience, and requires a lot of prior clinical knowledge; third, there are large differences in the results drawn by the same doctor in different states. difference

Fourth, human error is inevitable

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