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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

Published on: November 30, 2022

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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

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.

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

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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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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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