| 摘要: |
| 目前，医学图像作为临床检测以及放疗引导的重要参考依据，在医学的发展中起着关键作用。医学图像主要包括计算机断层扫描（CT）、核磁共振（MRI）、X射线、超声（US）等，超声相对前三者价格较低，对软组织成像效果较好且对人体基本无伤害，在现阶段应用已越来越广泛。超声图像分割对后期图像分析有很大的作用，可以给临床诊断及放疗摆位等提供一定的参考，本文就超声图像的分割的传统方法、基于形变模型的分割方法和结合深度学习方法的研究情况进行阐述。 |
| 关键词: 超声 图像分割 深度学习 |
| DOI： |
| 投稿时间：2021-11-02修订日期：2021-12-01 |
| 基金项目:常州市医学物理重点实验室项目(CM20193005),江苏省卫健委面上项目(M2020006) |
|
| The research status of ultrasound image segmentation |
| zhang fan,lu zheng da,li chun ying,ni xin ye,jiao zhu qing |
| () |
| Abstract: |
| At present, as an important reference for clinical detection and radiotherapy guidance, medical image plays a key role in the development of medicine. Medical images are mainly include Computed Tomography (CT), Magnetic Resonance Imaging(MRI), X-ray, Ultrasound (US). Compared with the first three, ultrasound is cheaper, has better effect on soft tissue imaging and nearly no harm to human body, so it has been more and more widely used at this stage. Ultrasound image segmentation plays an important role in late image analysis, and it can provide accurate reference for clinical diagnosis and radiotherapy placement. In this paper, the traditional segmentation methods, deformable model-based methods and deep learning methods of ultrasonic images are reviewed. |
| Key words: Ultrasound Image segmentation Deeping learning |
