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相关实验视频

Updated: Jun 13, 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

骨架引导的3D卷积神经网络用于管状结构细分.

Ruiyun Zhu¹, Masahiro Oda^2,3, Yuichiro Hayashi²

¹Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. rzhu@mori.m.is.nagoya-u.ac.jp.

International journal of computer assisted radiology and surgery

|September 12, 2024

概括

这项研究引入了一种新的3D深度学习网络,该网络使用骨架信息来改善医学成像中复杂的管状结构的细分. 骨架导向网络在CT扫描中的气道和腹动脉细分方面表现出卓越的准确性.

关键词:

三维卷积网络是3D卷积网络.这是一个CT图像CT图像CT图像CT图像.管状结构细分管状结构细分

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Last Updated: Jun 13, 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

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Published on: July 5, 2024

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

管状结构的精确细分对于临床应用至关重要,但由于其复杂性和数据不平衡而受到阻碍.
现有的方法在3D医学图像中难以处理复杂的分支和不同尺寸的管状结构.

研究的目的:

开发一个包含骨架信息的3D深度学习网络,以增强管状结构的细分.
提高医学成像中细分具有挑战性的解剖特征的准确性和稳定性.

主要方法:

使用3D卷积神经网络从体积CT图像中提取特征.
集成了一个新的骨架引导模块,用于在细分过程中保存和利用骨架信息.
为了有效的训练,提出了一种适应西格体的特维斯基损失函数,专门为骨细分量身定制.

主要成果:

对胸部和腹部CT数据集进行了实验,分别涉及90例和35例.
拟议的方法在两个数据集上显著优于以前的细分方法.
实现了高性能指标,包括93.0%的气道树长率和97.7%的腹动脉精度.

结论:

一个由骨架引导的3D卷积网络已成功开发,用于在3D医学图像中对管状结构进行细分.
拟议的网络有效地分割了小型和复杂的管状结构,超过了现有方法的性能.