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

TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.

Cell reports methods·2026
Same author

Corneal nerve regeneration: treatments and monitoring approaches.

Annals of medicine·2026
Same author

Toward a Random Background for Ligand Optimization.

bioRxiv : the preprint server for biology·2026
Same author

Optimizing outcomes in keratolimbal allograft for limbal stem cell deficiency.

Current opinion in ophthalmology·2026
Same author

Distinguishing Factors for Microbial Keratitis Groups: A Cross-Sectional Survey of US Cornea Specialists.

Cornea·2026
Same author

Hypervariable loop profiling decodes sequence determinants of antibody stability.

Nature structural & molecular biology·2026

相关实验视频

Updated: Jul 7, 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

2.8K

在白内障手术中用于瞳孔细分的自适应式张力特征提取.

Binh Duong Giap, Karthik Srinivasan, Ossama Mahmoud

    IEEE journal of biomedical and health informatics
    |December 21, 2023
    PubMed
    概括

    一种新的自适应波纹张量特征提取 (AWTFE) 方法在白内障手术视频中提高了自动瞳孔细分的准确性,通过克服照明和阻碍挑战来提高手术安全性和结果.

    科学领域:

    • 眼科医生 眼科 眼科
    • 计算机视觉 计算机视觉
    • 医疗成像医学成像

    背景情况:

    • 白内障手术是视觉显著白内障的唯一治疗方法,这是可预防失明的主要原因.
    • 稳定的瞳孔扩张对于成功的白内障手术至关重要.
    • 自动化瞳孔细分有助于识别手术期间瞳孔不稳定的风险.

    研究的目的:

    • 引入一种新的自适应波纹张量特征提取 (AWTFE) 方法,以提高基于深度学习的白内障手术中的瞳孔细分精度.
    • 解决诸如可变照明,仪器阻塞和影响瞳孔识别的透镜水分等挑战.

    主要方法:

    • 构建一个第三阶张量来表示空间,颜色和波形子带相关性.
    • 采用更高阶的奇数值分解来进行自适应冗余信息消除和学生特征估计.
    • 在BigCat和CaDIS数据集上使用深度学习模型评估AWTFE.

    主要成果:

    • 在CaDIS数据集上,AWTFE显著提高了细分性能,高达3.31%,在BigCat数据集上提高了2.26%.
    • 该方法在各种模型中实现了统计学上显著的改进 (P < 1.29 × 10-10),达到94.74% (BigCat) 和96.71% (CaDIS) 的子系数.
    • 在具有挑战性的外科手术阶段,AWTFE提高了高达2.87%的性能,并超过了其他特征提取技术.

    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

    2.4K
    Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation
    07:26

    Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation

    Published on: November 26, 2019

    8.1K

    相关实验视频

    Last Updated: Jul 7, 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

    2.8K
    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

    2.4K
    Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation
    07:26

    Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation

    Published on: November 26, 2019

    8.1K

    结论:

    • AWTFE方法有效地提取了相关的学生特征,提高了深度学习模型对学生细分的准确性.
    • 这种方法为改善白内障手术复杂环境中的自动化瞳孔识别提供了强大的解决方案.
    • AWTFE通过使瞳孔不稳定的术前风险检测更加可靠,为更安全的白内障手术做出了贡献.