Method, device and system for identifying and segmenting lymph node area of nasopharynx cancer

Abstract

The invention discloses a nasopharynx cancer lymph node area identification and segmentation method, device and system. The device comprises a data acquisition unit and an identification and segmentation unit. The system comprises an identification segmentation module and a data storage module. A magnetic resonance image to be identified and segmented is identified and segmented through an end-to-end coarse-to-fine three-dimensional deep supervised convolutional neural network three-dimensional model, so that a segmented region including a nasopharyngeal carcinoma lymph node is obtained, and the identification and segmentation method, device and system improve the accuracy of identification and segmentation of the nasopharyngeal carcinoma lymph node region; furthermore, according to the nasopharynx cancer lymph node area identification and segmentation method, device and system provided by the invention, the first training image data set is processed through a preset double-auditing data processing method to obtain the second training image data set, so that a reasonable model is designed fully according to the morphological characteristics of the lymph node, and the accuracy of identification and segmentation of the nasopharynx cancer lymph node area is improved. And the accuracy of identification and segmentation of the nasopharynx cancer lymph node region is improved.

Description

technical field [0001] The invention relates to the field of identification and segmentation of lymph node regions of nasopharyngeal carcinoma, and relates to a method, device and system for identification and segmentation of lymph node regions of nasopharyngeal carcinoma. Background technique [0002] Among newly diagnosed patients with nasopharyngeal carcinoma, metastatic lymph nodes are detected in approximately 70%-80% of cases at the time of first diagnosis. Accurate spatial modeling of metastatic lymph nodes is important for successful treatment. Artificial intelligence (AI) has been rapidly developed in the past decade, showing promising performance in the recognition and automatic segmentation of normal anatomical structures or lesions in medical images. Segmentation of regions of interest (ROI) or lesions is labor-intensive, both in imaging studies during radiation therapy and in delineation studies of the gross tumor volume (GTV), so it is necessary to reduce the ...

AI Technical Summary

Problems solved by technology

[0004] However, the existing technologies still have the following defects: the steps of these methods are cumbersome, the structure is not end-to-end, and a reasonable model is not fully designed according to the morphological characteristics of lymph nodes

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