Detection method for medical image segmentation and medical image segmentation method and device

A technology of medical images and detection methods, applied in the field of image processing, can solve the problem of not forming end-to-end

Active Publication Date: 2020-01-03
ZHONGAN INFORMATION TECH SERVICES CO LTD
View PDF6 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But this method has some disadvantages: it belongs to step-by-step training in the training stage, and cannot form an end-to-end (end-to-end) structure; in addition, in the segmentation step, the information outside the detection area is also discarded, and these information are in the accurate Segmentation often plays a non-negligible role

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Detection method for medical image segmentation and medical image segmentation method and device
  • Detection method for medical image segmentation and medical image segmentation method and device
  • Detection method for medical image segmentation and medical image segmentation method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Please refer to figure 1 , the present application discloses a detection method for medical image segmentation, which includes steps S110-S150, which will be described respectively below.

[0037] Step S110, acquiring a medical image of a target object and a feature map corresponding to the medical image, where the target object is a lesion formed on a tissue or organ.

[0038] In this embodiment, common medical imaging equipment can be used to shoot specific tissues and organs of the patient, so as to obtain medical images such as CT images, MRI images, PET images, and DSA images, and the target object can be kidney tumors , Lung tumors, liver tumors, stomach tumors and other types of lesions, these lesions are often formed on the internal tissues and organs of patients, and have the characteristics of small targets (often in millimeters in diameter) and low discrimination.

[0039] Step S120, using the convolutional neural network to process the feature map to obtain...

Embodiment 2

[0057] Please refer to Figure 4 , on the basis of the detection method for medical image segmentation disclosed in Embodiment 1, a medical image segmentation method is also disclosed, the medical image segmentation method includes steps S210-S230, which will be described respectively below.

[0058] Step S210, acquiring a medical image of a target object, where the target object is a lesion formed on a tissue or organ.

[0059] For example, common medical imaging equipment can be used to shoot specific tissues and organs of patients to obtain medical images such as CT images, MRI images, PET images, and DSA images, and the target objects can be kidney tumors, lung tumors, Liver tumors, gastric tumors and other types of lesions, which are often formed on the internal tissues and organs of patients, have the characteristics of small targets (often in millimeters in diameter) and low discrimination.

[0060] In step S220, the medical image is input into a pre-established lesion...

Embodiment 3

[0090] Please refer to Figure 8 , on the basis of the medical image segmentation method for medical images disclosed in Embodiment 2, this application discloses a target recognition device 4 for medical images, which mainly includes an acquisition unit 41, a model processing unit 42, a recognition Unit 43 will be described separately below.

[0091] The acquiring unit 41 is configured to acquire a medical image of a target object, where the target object is a lesion formed on a tissue or organ. In this embodiment, common medical imaging equipment can be used to shoot specific tissues and organs of the patient, so as to obtain medical images such as CT images, MRI images, PET images, and DSA images, and the target object can be kidney tumors , Lung tumors, liver tumors, stomach tumors and other types of lesions, these lesions are often formed on the internal tissues and organs of patients, and have the characteristics of small targets (often in millimeters in diameter) and lo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a detection method for medical image segmentation and a medical image segmentation method and device. The detection method comprises the steps of obtaining a medical image of atarget object and a feature map corresponding to the medical image; processing the feature map by using a convolutional neural network to obtain feature information of the feature map; generating a weight table with the same size as the feature map according to the obtained bounding box; multiplying the weight table by the feature map bit by bit to obtain an optimized feature map, the optimized feature map being used for detecting and obtaining a formation area of a target object during medical image segmentation. The weight table with the same size as the feature map is generated according to the obtained bounding box and multiplied into the feature map in the form of the weight, so that the formation area of the target object can be accurately generated in the obtained optimized featuremap, and the segmentation accuracy of the small target lesion can be improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a detection method for medical image segmentation, a medical image segmentation method and a device. Background technique [0002] Artificial intelligence deep learning technology has been increasingly used in various fields due to the rapid development in recent years, among which the convolutional neural network (CNN) model is one of the most important methods in deep learning technology, in classification, detection and Remarkable achievements have been made in aspects such as segmentation, as well as in the field of medical images. The convolutional neural network model is often composed of multiple layers of neurons, so it has a strong feature learning ability. The learned network model has a good representation ability for the original data, so that the internal data of the data can be extracted through large-scale training data. The rich features are conducive to...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/00G06N3/08G06N3/04
CPCG06T7/0012G06T7/11G06N3/08G06T2207/30084G06T2207/30096G06T2207/10081G06N3/045
Inventor 郭延恩
Owner ZHONGAN INFORMATION TECH SERVICES CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap