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相关概念视频

External Anatomy of the Kidney01:21

External Anatomy of the Kidney

The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...
Internal Anatomy of the Kidney01:12

Internal Anatomy of the Kidney

The kidneys are essential organs in the human body, performing a myriad of tasks that maintain homeostasis and overall health.
Anatomical Position and Dimensions
The kidneys are retroperitoneal organs positioned against the posterior abdominal wall on either side of the spine, roughly between the twelfth thoracic and third lumbar vertebrae. Each kidney is typically 10-12 cm long, 5-6 cm wide, and 3-4 cm thick, weighing about 150 grams.
Renal Cortex
The outermost region of the kidney is the...
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

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相关实验视频

Updated: May 21, 2026

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

使用VHUCS-Net与突起检测网络的高级质细分使用VHUCS-Net.

J Jenifa Sharon1, L Jani Anbarasi1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in artificial intelligence
|February 20, 2026
PubMed
概括
此摘要是机器生成的。

新的VHUCS-Net架构准确地细分脏结构和质量,提高了脏疾病的诊断效率. 这种人工智能模型通过精确和可解释的医学图像细分来增强临床决策.

关键词:
检测异常检测异常的检测计算机辅助诊断是指计算机辅助的诊断.混合深度学习是混合深度学习.质量细分 细分 质量细分突起检测网络的突起检测网络.语义细分 语义细分 语义细分 语义细分变压器增强的U-Net模型视觉变压器 视觉变压器

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

相关实验视频

Last Updated: May 21, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 准确的细分质量和结构对于诊断和治疗至关重要.
  • 现有的方法可能会在复杂的结构特征和图像中精确的边界识别方面扎.

研究的目的:

  • 引入双轨混合VHUCS-Net架构,以增强质量和结构细分.
  • 为了提高细分输出的准确性和可解释性,用于临床决策支持.

主要方法:

  • 集成了一个变压器增强的U-Net与一个对比度优化的突起检测网络 (PDN).
  • 利用视觉转换器的注意力和高分辨率网络 (HRNet) 进行全球和高分辨率的特征捕捉.
  • 在PDN中采用多尺度聚合,对比度增强和特征融合,以实现精确的质量细分.

主要成果:

  • 在脏细分数据集上获得了0.9441的交叉与联盟 (IoU) 分数和0.9712的子系数.
  • 展示了出色的细分精度,突出了结构尺寸-形状变体,边界和复杂的特征.
  • 通过使用额外的公共数据集,在多个细分任务中验证了通用性.

结论:

  • VHUCS-Net显著提高了诊断效率,并支持临床决策.
  • 该模型为病分析提供准确,可解释的细分输出.
  • 拟议的架构证明了对各种医学图像细分任务的有效性和通用性.