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Imaging Studies II: Ultrasonography

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

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Updated: May 6, 2026

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

启动U-Net用于增强乳房超声波图像细分使用转移学习.

Yeonhyo Choi¹, Myoung Nam Kim², Sungdae Na³

¹Department of Medical & Biological Engineering, Graduate School, Kyungpook National University, Daegu 41404, Republic of Korea.

Bioengineering (Basel, Switzerland)

|February 27, 2026

概括

这项研究通过将Inception模块集成到U-Net架构中来增强超声图像中的乳腺癌细分. 改进的模型显示了~5%的性能提升,推进了自动化医疗图像分析.

关键词:

这就是U-Net.乳房超声波乳房超声波是什么?深度学习是一种深度学习.图像细分图像细分开始的开始的开始.医学成像医学成像转移学习转移学习

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科学领域:

医疗成像医学成像
人工智能在医学中的应用
图像细分图像细分

背景情况:

乳腺癌的诊断严重依赖于超声波成像.
操作员的依赖性和图像质量问题阻碍了传统方法.
现有的U-Net模型在特征提取方面存在局限性,原因是编码器的浅度.

研究的目的:

开发乳房超声波图像的增强细分模型.
改进超越传统U-Net架构的功能提取能力.
为了利用转移学习来提高细分性能.

主要方法:

用Inception架构替换了U-Net编码器.
使用 ImageNet 预先训练的权重进行转移学习.
在900张乳房超声波图像上训练和评估模型.

主要成果:

开始U-Net实现了0.7774的IOU和0.8491的子得分.
与基线U-Net.net相比,显示出大约5%的改善.
获得了0.7081的精度和0.7174.4的回忆.

结论:

开始模块增强了用于乳房超声波细分的特征提取.
从ImageNet转移学习是有效的,尽管领域差异.
这种方法为先进的医学成像应用提供了基础.