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Lung cancer-oriented intelligent target region and organ-at-risk segmentation method

A target area and organ technology, applied in the field of image processing, can solve problems such as waste of computing power and marked data, low execution efficiency, and cumbersomeness, and achieve the effect of alleviating data hunger and enhancing segmentation accuracy

Pending Publication Date: 2021-12-14
TIANJIN UNIV
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Problems solved by technology

This process is more cumbersome, the execution efficiency is low, and it is also a waste of computing power and labeled data

Method used

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  • Lung cancer-oriented intelligent target region and organ-at-risk segmentation method
  • Lung cancer-oriented intelligent target region and organ-at-risk segmentation method
  • Lung cancer-oriented intelligent target region and organ-at-risk segmentation method

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Embodiment Construction

[0024] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0025] The present invention optimizes the nnU-Net basic frame network, introduces conditional strategies to break the labeling barriers between marked data, and realizes common learning between different data sets. In order to further utilize the images between the various datasets, an uncertainty-aware self-augmentation model is introduced to assist the segmentation of organs at risk. In order to better fit the learning performance of the segmentation model, two joint loss functions were designed to optimize the segmentation tasks of target regions and organs at risk, and improve the segmentation accuracy and generalization ability of the learning model.

[0026] The specific imple...

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Abstract

The invention discloses a lung cancer-oriented intelligent target region and organ-at-risk segmentation method. Dual-path segmentation is performed by adopting a common segmentation network framework of a lung cancer-oriented target region and an organ at risk; the common segmentation network framework comprises a teacher model and a student model, and the two models both adopt nnU-Net as trunks; and for the organ at risk and the target region, pre-defined auxiliary condition information is respectively given to be embedded in an up-sampling process and is connected to a corresponding decoder in a decoding process, and predicted masks are fused to obtain a final segmentation result. According to the method, the target region and the organ at risk can be accurately and efficiently segmented.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an intelligent segmentation method for lung cancer target areas and organs at risk. Background technique [0002] As an important means of lung cancer treatment, radiation therapy can help improve the clinical cure rate of patients with advanced non-small cell lung cancer, prolong the life cycle of patients, and improve the quality of life of patients. The main purpose of radiation therapy is to deliver a curative dose to the tumor target while protecting the organs at risk from radiation to avoid complications. When making a treatment plan, it is necessary to accurately outline the outline of the target area to determine the appropriate dose to ensure the inactivation of the target area. In addition, pathologically at-risk organs with low radiation resistance must be accurately delineated to ensure that they are not exposed to additional doses, since radiation-induced ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/08G06T2207/10081G06T2207/30061G06T2207/30096G06N3/045
Inventor 杨志永张国彬姜杉
Owner TIANJIN UNIV
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