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1. CN111784706 - Automatic nasopharyngeal carcinoma primary tumor image identification method and system

Office
China
Application Number 202010595992.2
Application Date 28.06.2020
Publication Number 111784706
Publication Date 16.10.2020
Grant Number 111784706
Grant Date 04.06.2021
Publication Kind B
IPC
G06T 7/11
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
11Region-based segmentation
G06T 7/30
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
30Determination of transform parameters for the alignment of images, i.e. image registration
G06T 7/33
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
30Determination of transform parameters for the alignment of images, i.e. image registration
33using feature-based methods
G06K 9/34
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
20Image acquisition
34Segmentation of touching or overlapping patterns in the image field
G06T 7/136
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
136involving thresholding
CPC
G06T 7/344
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
30Determination of transform parameters for the alignment of images, i.e. image registration
33using feature-based methods
344involving models
G06T 7/30
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
30Determination of transform parameters for the alignment of images, i.e. image registration
G06T 7/11
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
11Region-based segmentation
G06T 7/136
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
136involving thresholding
G06T 2207/10081
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
10Image acquisition modality
10072Tomographic images
10081Computed x-ray tomography [CT]
G06T 2207/10088
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
10Image acquisition modality
10072Tomographic images
10088Magnetic resonance imaging [MRI]
Applicants GUANGZHOU PERCEPTION VISION MEDICAL TECHNOLOGIES CO., LTD.
广州柏视医疗科技有限公司
GUANGZHOU BAISHI DATA TECHNOLOGY CO., LTD.
广州柏视数据科技有限公司
Inventors WEI JUN
魏军
ZHU DEMING
朱德明
XIE PEILIANG
谢培梁
Agents 北京兴智翔达知识产权代理有限公司 11768
Title
(EN) Automatic nasopharyngeal carcinoma primary tumor image identification method and system
(ZH) 鼻咽癌原发肿瘤图像自动识别方法及系统
Abstract
(EN) The embodiment of the invention provides an automatic nasopharyngeal carcinoma primary tumor image identification method and system. Multi-modal input of a semantic segmentation network model is formed based on a CT three-dimensional image of a tested person and an MR sequence three-dimensional image, and identification of a nasopharyngeal carcinoma primary tumor on CT is realized. By combining the CT three-dimensional image and the MR sequence three-dimensional image, the quality of input data can be effectively improved, global information and detail information of a high-resolution image can be learned, the prediction accuracy and generalization ability of the semantic segmentation network model can be effectively improved, the flexibility of an input end and an output end is achieved,and the working efficiency of medical workers is effectively improved.
(ZH) 本发明实施例提供了一种鼻咽癌原发肿瘤图像自动识别方法及系统,以被测者的CT三维图像为基础,辅以MR序列三维图像,构成了语义分割网络模型的一个多模态输入,实现在CT上的鼻咽癌原发肿瘤识别。结合CT三维图像与MR序列三维图像,能有效提高输入数据的质量,并学习到高分辨率图像的全局信息和细节信息,能有效提高语义分割网络模型的预测准确度和泛化能力,并具备输入端与输出端的灵活性,进而有效提高医疗工作者的工作效率。
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