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Segmentation and recognition of breast ultrasound images based on an expanded U-Net
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 6, p e0253202 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images.
- Subjects :
- Computer science
02 engineering and technology
Overfitting
Diagnostic Radiology
030218 nuclear medicine & medical imaging
Machine Learning
0302 clinical medicine
Ultrasound Imaging
Breast Tumors
Medicine and Health Sciences
0202 electrical engineering, electronic engineering, information engineering
Redundancy (engineering)
Segmentation
Breast
Breast ultrasound
Multidisciplinary
medicine.diagnostic_test
Radiology and Imaging
Robotics
Oncology
Medicine
Engineering and Technology
Female
020201 artificial intelligence & image processing
Ultrasonography, Mammary
Robots
Research Article
Computer and Information Sciences
Imaging Techniques
Science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Breast Neoplasms
Surgical and Invasive Medical Procedures
Context (language use)
Minimally Invasive Surgery
Research and Analysis Methods
03 medical and health sciences
Deep Learning
Sørensen–Dice coefficient
Diagnostic Medicine
Artificial Intelligence
Image Interpretation, Computer-Assisted
Breast Cancer
medicine
Humans
Benign Tumors
Models, Statistical
business.industry
Mechanical Engineering
Deep learning
Cancers and Neoplasms
Pattern recognition
Image segmentation
Artificial intelligence
business
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....b8acdb5610b427b9fd30456cea9084ec