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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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What is an Electrochemical Gradient?01:26

What is an Electrochemical Gradient?

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Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...
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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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相关实验视频

Updated: Feb 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于深度学习的肝脏瘤细分从计算机断层扫描用梯度增强网络扫描.

Hangyeul Shin1, Kyujin Han2, Seungyoo Lee3

  • 1School of Applied Artificial Intelligence and Entrepreneurship, Handong Global University, Pohang 37554, Republic of Korea.

Diagnostics (Basel, Switzerland)
|February 13, 2026
PubMed
概括

本研究介绍了一种使用G-UNETR++网络的自动化肝脏瘤细分方法. 该方法实现了高精度,优于现有的模型,用于改进肝癌诊断.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.梯度增强网络的梯度增强网络是指梯度增强的网络.肝脏瘤细分 肝脏瘤细分

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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 肝脏瘤带来了重大的诊断挑战.
  • 准确的细分对于有效的治疗计划至关重要.
  • 现有的细分方法往往需要人工干预.

研究的目的:

  • 开发一种全自动的肝脏瘤细分方法.
  • 为了利用渐变增强网络G-UNETR++.
  • 为了提高肝癌的诊断能力.

主要方法:

  • 在CT扫描上利用G-UNETR++进行肝脏和瘤细分.
  • 实施了掩盖策略,以将细分集中在肝脏区域.
  • 在LiTS和3DIRCADb数据集上训练并验证了模型.

主要成果:

  • 在LiTS数据集上获得了0.844的平均子得分.
  • 在3DIRCADb数据集上获得了0.832的平均子得分.
  • 在肝癌细分方面表现优于最先进的模型.

结论:

  • 开发的基于G-UNETR++的方法提供了有效的自动肝脏瘤细分.
  • 该方法在不同的数据集中显示出强大的通用性.
  • 这种工具可以帮助医生诊断肝脏瘤和计划治疗.