Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI.

ArXiv·2026

Same author

Toward Practical Solid-State Lithium Batteries With High-Nickel Cathodes: An Interface-Centered Perspective.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Generative Artificial Intelligence and Large Language Models in Clinical Oncology.

MedComm·2026

Same author

Investigation on the unsteady aerodynamic coefficients of iced conductors and the applicability of quasi-static assumptions.

Scientific reports·2026

Same author

GPR15-guided CD8<sup>+</sup> T regulatory cells control intestinal inflammation.

Nature·2026

Same author

Self-Limiting Covalent Ligation Mechanism Enabling Anomalously High Interfacial Compatibility in Organic-in-Sulfide All-Solid-State Lithium Batteries.

Angewandte Chemie (International ed. in English)·2026

Search research articles

相关实验视频

Updated: Jul 26, 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

两个阶段的多任务深度学习框架,用于从CT图像中同时进行盆骨细分和地标检测.

Haoyu Zhai¹, Zhonghua Chen², Lei Li³

¹School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, 116024, China.

International journal of computer assisted radiology and surgery

|June 15, 2023

概括

这项研究引入了一种两阶段的算法,该算法增强了盆骨细分和CT扫描中的里程碑检测,提高了患病病例的准确性. 这种方法提供了精确的解剖界限,对于全关节整形术的规划至关重要.

关键词:

骨段化骨段化骨段化骨段化从粗到细的战略是粗到细的战略.里程碑检测检测地标的检测多任务网络多任务网络.

更多相关视频

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

相关实验视频

Last Updated: Jul 26, 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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

科学领域:

医疗成像医学成像
计算机辅助手术计算机辅助手术
整形外科手术整形外科手术

背景情况:

精确的盆骨细分和里程碑识别对于在全关节整形手术中进行术前规划至关重要.
患病的盆腔解剖学往往会损害标准细分和检测方法的精度,可能导致手术并发症.

研究的目的:

开发和验证一种新的两阶段,多任务算法,以改善盆骨细分和地标检测.
为了提高准确性,特别是在患有疾病的骨盆解剖病例中,解决当前临床实践中的关键局限性.

主要方法:

一个粗细的,两阶段的框架,采用多任务学习来同时进行细分和地标检测.
第一个阶段使用双重任务网络进行全球分析,然后在第二阶段使用边缘增强的双重任务网络进行本地精细化和边界划分.

主要成果:

该算法在骨盆结构方面获得了高的子相似系数 (DSC) 评分 (例如,左/右部为0.97),平均基准误差为3.24毫米.
第二阶段显著提高了对现有方法的acetabular边界细分精度5.42%,超过了最先进的方法.
整个细分和检测工作流程大约在10秒内完成,证明了计算效率.

结论:

拟议的多任务,粗到细的策略显著提高了盆骨细分和里程碑检测的准确性,特别是在患病的部图像.
这一进步有助于更精确,更快速地进行手术前规划,以设计和植入骨杯假体.