Jove
Visualize
联系我们
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

Artificial intelligence in microscopic hair imaging for scalp disorders: From image acquisition to clinical decisions.

Medical image analysis·2026
Same author

Enhancing the Interpretation of Skin Lesion Diagnosis: Concept Adaptive Fine-Tuning of Vision-Language Models.

IEEE journal of biomedical and health informatics·2025
Same author

Compact CNN module balancing between feature diversity and redundancy.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

HDConv: Heterogeneous kernel-based dilated convolutions.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Precise Phase Measurement for Fringe Reflection Technique through Optimized Camera Response.

Sensors (Basel, Switzerland)·2023
Same author

A literature survey of MR-based brain tumor segmentation with missing modalities.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2022

相关实验视频

Updated: Jun 27, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.7K

扩展异质卷积用于基于R-CNN面具的细胞检测和细分.

Fengdan Hu1, Haigen Hu1, Hui Xu1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了R-DHCNN口罩,这是一种用于准确检测和细分细胞的新方法. 它使用扩展异质卷积 (DHConv) 来克服细胞变异性和计算负载的挑战,提高生物细胞数据集的性能.

关键词:
细胞检测和细胞细分.扩张 卷积 卷积不同质的卷积卷积.面罩 R-CNN 面膜

更多相关视频

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

542
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

524

相关实验视频

Last Updated: Jun 27, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.7K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

542
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

524

科学领域:

  • 生物医学成像学 生物医学成像学
  • 计算机视觉 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 精确的细胞检测和细分对于生物研究至关重要,但由于细胞的变异性 (形状,大小,灰度) 和密集分布,它们具有挑战性.
  • 像Mask R-CNN这样的标准方法面临着资源有限的显微镜成像设备的局限性,原因是高计算负担和众多的学习参数.

研究的目的:

  • 开发一种有效和准确的细胞检测和细分方法,解决现有方法的局限性.
  • 引入一种新型的卷积模块 - - 扩展异质卷积 (DHConv),以提高对不同细胞特征的适应性.

主要方法:

  • 提出面膜R-DHCNN,将一个新的扩展异质卷积 (DHConv) 模块集成到面膜R-CNN框架中.
  • DHConv 结合了异质核结构和扩展卷积,以更好地处理细胞形状和大小的变化.
  • 用DHConv模块替换Mask R-CNN中的传统同质卷积,以进行增强的细胞分析.

主要成果:

  • 拟议的面具R-DHCNN方法在多个指标上表现出卓越的性能,包括平均精度 (AP),精度,回忆,子系数和全视质量 (PQ).
  • 在各种生物细胞数据集 (U373,GoTW1,SIM+,T24) 上进行的实验验证实了DHConv模块的有效性.
  • 该方法在保持高效的计算成本 (FLOPs) 和处理速度 (FPS) 的同时获得了竞争力的结果.

结论:

  • 面具R-DHCNN方法在细胞检测和细分方面取得了重大进展,特别是在具有挑战性的生物成像场景中.
  • 新的DHConv模块有效地解决了细胞可变性和计算约束所带来的局限性.
  • 这种方法为显微镜中自动化细胞分析提供了更强大,更有效的解决方案.