Label-free pancreatic image automatic segmentation system based on adversarial learning

An automatic segmentation and image segmentation technology, applied in image analysis, neural learning methods, image enhancement, etc., can solve the problem that unlabeled data is difficult to be used, improve the learning ability of the underlying features, solve complex and changeable structures, and network memory The effect of access reduction

Active Publication Date: 2021-12-31
ZHEJIANG UNIV
View PDF15 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an automatic segmentation system for un

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
  • Label-free pancreatic image automatic segmentation system based on adversarial learning
  • Label-free pancreatic image automatic segmentation system based on adversarial learning
  • Label-free pancreatic image automatic segmentation system based on adversarial learning

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0053] The data quality alignment module performs image normalization preprocessing on pancreatic CT image data to reduce the data heterogeneity among different sources of pancreatic CT image data, and selects the selected pancreatic CT image data from the region of interest screening, super-resolution reconstruction, Data amplification, gray level normalization, etc. are used for quality alignment. For the labeled pancreatic CT image data, the same normalization preprocessing is performed on the labels. The specific implementation method is:

[0054] Screening of regions of interest: including effective abdominal range framing and level of interest screening; different sources of pancreatic CT images have certain differences in shooting FOV (Field of View), and the abdominal range and viewing angle height of the captured pancreatic CT images are different. After binarizing the pancreatic CT image, measure the image area attributes of the two-dimensional CT image to find all ...

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 label-free pancreatic image automatic segmentation system based on adversarial learning, and the system employs an adversarial learning method to search label image data and common image features in label-free image data, and strengthens the personalized image features of the label-free image data, and a pancreas image segmentation model suitable for the label-free pancreas CT image is constructed; a Transform structure is introduced to segment a pancreas CT image, pixel block partition processing is performed on pancreas CT image data, a self-attention mechanism is added to establish a long-connection cross-correlation relation between pixel blocks, a residual structure is used in a multi-stage encoder-decoder structure, multi-scale pancreas target image features can be subjected to weighted interaction, and the segmentation effect on small targets such as pancreatic tissues is obviously improved. According to the method, a reliable segmentation result can be given for label-free pancreas CT image data, the film reading time of a doctor is effectively shortened, the diagnosis and treatment process of pancreas-related diseases is optimized, and the diagnosis and treatment efficiency of the doctor is improved.

Description

technical field [0001] The invention belongs to the technical field of image data processing, and in particular relates to an automatic segmentation system of unlabeled pancreas images based on adversarial learning. Background technique [0002] Pancreas-related diseases develop rapidly clinically and have poor prognosis. Pancreatic cancer is a highly malignant digestive tract tumor with a 5-year survival rate of less than 5%. Early detection of pancreatic lesions and precise treatment are crucial to improving the quality of life of patients with pancreatic-related diseases. Pancreatic cancer treatment options currently mainly include surgical resection and neoadjuvant therapy, both of which require precise positioning of pancreatic tissue before surgery. Abdominal computed tomography (Computed Tomography) is an important examination method in the diagnosis process of pancreas-related diseases. The automatic segmentation tool of pancreatic CT images can assist radiologists ...

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
IPC IPC(8): G06T7/00G06T7/11G06T9/00G06T3/40G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T9/002G06T3/4007G06T3/4046G06T3/4053G06N3/04G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06F18/213G06F18/253G06F18/214
Inventor 李劲松朱琰田雨周天舒
Owner ZHEJIANG UNIV
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