Jove
Visualize
联系我们

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

Deep Unfolded Variable Projection Networks.

International journal of neural systems·2025
Same author

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis.

IEEE open journal of engineering in medicine and biology·2024
Same author

Variable Projection Support Vector Machines and Some Applications Using Adaptive Hermite Expansions.

International journal of neural systems·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: May 21, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K

一个基于多维粒子群优化算法用于脑MRI瘤细分.

Zsombor Boga1, Csanád Sándor1, Péter Kovács2

  • 1Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种先进的粒子集群优化 (PSO) 技术,用于MRI扫描中的脑瘤细分. 该方法自动确定分段级别,以更少的训练数据提高准确性.

关键词:
分段的适应性数量.大脑瘤的细分 脑瘤的细分集群集成是指集群集成.图像分割 图像细分 图像细分磁共振成像技术的使用多维粒子群集优化 多维粒子群集优化随机森林分类器随机森林分类器

更多相关视频

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.2K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K

相关实验视频

Last Updated: May 21, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.2K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 图像细分对于医学图像分析至关重要,特别是用于识别诸如脑瘤等病理.
  • 传统的细分方法通常需要手动的参数调整和预定义的细分数,从而限制了它们的适应性.
  • 现有的方法可能会与复杂的数据 (如多模态核磁共振) 扎,需要更强大的技术.

研究的目的:

  • 开发一个自动化的,基于集群的脑瘤细分算法,使用一个多维的粒子集群优化 (PSO).
  • 通过将PSO与随机森林分类器 (RFC) 集成来提高细分精度.
  • 减少对大型标记数据集的依赖,以训练准确的瘤细分模型.

主要方法:

  • 实施多维PSO变体用于无监督的基于集群的图像细分.
  • 从多模态MRI数据中整合灰度强度和空间信息.
  • 将初始细分与随机森林分类器 (RFC) 集成,以获得精细的结果.
  • 使用RSNA-ASNR-MICCAI脑瘤细分 (BraTS) 挑战数据集进行验证.

主要成果:

  • 拟议的算法自动确定最佳的细分细分度,而无需预定义的细分数.
  • 该方法通过利用多模态MRI数据和空间信息,有效地隔离脑瘤.
  • 与RFC集成显著提高了细分精度.
  • 在BraTS数据集上取得了强大的结果,减少了对广泛标记训练数据的需求.

结论:

  • 开发的基于PSO的方法为脑瘤细分提供了一种高效和准确的方法.
  • 自动选择细分细分度和多模式数据集成提高了临床相关性.
  • 这种方法为医疗图像分析中有限的标记训练数据的场景提供了有希望的替代方案.