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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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相关实验视频

Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

多尺度多对象半监督一致性学习用于超声图像细分的超声图像细分.

Saidi Guo¹, Zhaoshan Liu², Ziduo Yang³

¹School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China; School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.

Neural networks : the official journal of the International Neural Network Society

|January 4, 2025

概括

此摘要是机器生成的。

超声波图像分割的半监督学习 (SSL) 得到了我们新的MSMO框架的改进. 它有效地融合了多个规模的上下文和空间信息,减少了临床工具的手动注释负担.

关键词:

一致的学习学习一致的学习多个对象的多个对象.多个尺度的多个尺度.半监督学习半监督学习超声波图像细分超声波图像细分

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Last Updated: Jun 4, 2025

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科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

超声波图像的手动注释是耗时且资源密集的.
半监督学习 (SSL) 利用未标记的数据来提高模型性能,而标记的数据有限.
现有的SSL方法难以融合多层次的上下文信息,并在多对象分割中处理空间偏差.

研究的目的:

引入一个新的半监督学习框架,MSMO,以改进超声波图像细分.
为了应对SSL中多层次上下文融合和多对象空间信息偏差的挑战.
为了减少超声波图像分析中的手动注释负担.

主要方法:

开发了一个基于一致性学习的多规模多对象 (MSMO) 半监督框架.
采用了具有注意模块的上下文感知编码器,用于多级特征提取.
使用HConvLSTM解码器进行对象校准和递归多对象语义融合.
应用一致性约束来最大限度地减少变化,并改善不确定的地区的预测.

主要成果:

拟议的MSMO框架在四个基准数据集上显著超过了基准SSL方法.
在单对象和多对象超声波图像分割任务中实现了卓越的性能.
证明了多层次上下文和空间信息的有效融合.

结论:

MSMO为自动超声波图像细分提供了一个有前途的解决方案,减少了对手工注释的依赖.
该框架显示出作为医学图像分析临床工具的巨大潜力.
开发的MSMO框架提高了超声波图像细分的效率和准确性.