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相关概念视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography04:48

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

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An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised...
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We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of...
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This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament...
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Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography...
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相关实验视频

Updated: Jan 20, 2026

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

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深度扩展卷积适配器用于医疗物体跟踪和细分使用任何细分模型2的细分.

Guoping Xu1, Christopher Kabat1, You Zhang1

  • 1The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Machine learning: science and technology
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了DD-SAM2,这是一个高效的框架,用于调整SegmentAnything Model 2 (SAM2) 用于医疗视频细分和跟踪. 这种方法增强了特征提取,在有限的数据中实现了高性能.

更多相关视频

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相关实验视频

Last Updated: Jan 20, 2026

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

Published on: November 30, 2022

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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

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科学领域:

  • 医疗成像医学成像
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习驱动医学图像细分,但与动态场景和模式特定设计作斗争.
  • 细分任何模型2 (SAM2) 提供实时视频细分,但需要广泛的数据用于医疗适应.
  • 当前的方法面临着高的计算成本,并在适应SAM2到医疗视频时面临灾难性的遗忘风险.

研究的目的:

  • 提出DD-SAM2,这是医疗视频分割和跟踪中的Segment Anything Model 2 (SAM2) 的一个高效的适应框架.
  • 为了增强SAM2的多尺度特征提取,使用深度扩展适配器 (DD-Adapter) 以最小的参数开销.
  • 为了使SAM2在医疗视频上使用有限的训练数据进行有效的微调,并利用其流媒体内存进行对象跟踪.

主要方法:

  • 开发了DD-SAM2,这是SAM2的高效适应框架.
  • 包含一个深度扩展适配器 (DD-Adapter) 来改善多尺度特征提取.
  • 利用SAM2的流媒体内存进行医疗视频对象跟踪和细分.

主要成果:

  • 在医疗视频细分和跟踪任务中实现了卓越的性能.
  • 证明了高的子得分: 0.93±0.04 在TrackRad2025 (瘤细分) 和 0.97±0.01 在EchoNet-Dynamic (左心室跟踪).
  • 在医疗视频上展示了SAM2的有效微调,但培训数据有限.

结论:

  • DD-SAM2提供了一种高效的解决方案,用于将SAM2适应医疗视频细分和跟踪.
  • 拟议的DD-Adapter增强了特征提取,以最小的参数实现高性能.
  • 这项工作代表了在医疗视频分析中基于适配器对SAM2进行微调的新探索.