Three-dimensional dose distribution prediction method for nasopharyngeal carcinoma based on segmentation task

Abstract

The present invention relates to a three-dimensional dose distribution prediction method for nasopharyngeal carcinoma based on segmentation task assistance, which specifically includes: collecting original nasopharyngeal carcinoma images and labeling them; constructing a dose distribution prediction model, the prediction model including an auxiliary segmentation network, The dose prediction network and the confrontation network; the segmentation network and the prediction network share the encoder network parameters, and the shared representation information between the two is obtained by jointly training the segmentation task and the dose prediction task, which enhances the feature expression ability of the shared encoder and promotes the network in Under the limited training data, the essential features that have auxiliary functions for dose prediction in the segmentation task are mined to the maximum extent. At the same time, in order to effectively utilize the feature information at different scales of the prediction decoder, the present invention proposes a multi-scale iterative fusion IMF strategy at the prediction task decoder side to obtain more accurate prediction results.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a three-dimensional dose distribution prediction method for nasopharyngeal carcinoma based on segmentation task assistance. Background technique [0002] Medical image segmentation is an important field of image processing. The purpose of this field is to segment the important information of the patient's condition in medical images and provide a scientific basis for clinical diagnosis and treatment, which is of great value for doctors to judge the condition. In tumor treatment, radiotherapy is the most effective and safest treatment technology for patients' health, and its non-invasive and low-toxicity characteristics have also been recognized by the medical community. Adhering to the prescribed dose of radiation therapy is the key to successful treatment. However, the dose control in the clinical radiotherapy process is a difficult subject, and the existing research results are...

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Problems solved by technology

However, these two prediction models also have their own shortcomings. The former does not consider the impact of the organs at risk around the target area, and the latter cannot effectively use the anatomical characteristics of the tumor and organs at risk.

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