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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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利用转移学习驱动的卷积神经网络基础的语义细分模型用于使用MRI图像进行医学图像分析.

Amal Alshardan1, Nuha Alruwais2, Hamed Alqahtani3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. amalshardan@pnu.edu.sa.

Scientific reports
|December 18, 2024
PubMed
概括

这项研究引入了一种新的基于语义分割的深度转移学习医疗图像分析 (DTLSS-MIA) 技术,用于在MRI扫描中准确的脑瘤 (BT) 分段. DTLSS-MIA方法的准确率达到99.53%,改善了早期诊断和治疗规划.

关键词:
大脑瘤是什么?类鱼的优化优化在DeepLabv3+中使用.磁共振成像 磁共振成像 磁共振成像 磁共振成像医学图像 医学图像语义细分 语义细分是指语义细分.

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科学领域:

  • 医学图像分析 医学图像分析
  • 医疗保健中的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 从磁共振图像 (MRI) 进行脑瘤 (BT) 细分对于及时诊断和治疗至关重要,但由于组织复杂性,仍然具有挑战性.
  • 放射科医生的手动细分是繁的,容易出现错误.
  • 现有的深度学习 (DL) 模型,特别是卷积网络,在编码解码过程中面临信息丢失和参数复杂性的挑战.

研究的目的:

  • 介绍一种新的基于语义分割的深度转移学习医疗图像分析 (DTLSS-MIA) 技术,用于MRI中精确的脑瘤细分.
  • 解决现有的深度卷积网络在处理信息丢失和参数复杂性方面的局限性.

主要方法:

  • DTLSS-MIA技术使用中位过 (MF) 来优化MRI图像质量和降低噪音.
  • 带有EfficientNet骨干的DeepLabv3+用于语义细分,以识别受影响的大脑区域.
  • 囊网络 (CapsNet) 架构用于特征提取.
  • 鱼优化 (CFO) 算法调整扩散变量自编码器 (D-VAE) 的超参数进行分类.

主要成果:

  • DTLSS-MIA技术在MRI扫描中细分脑瘤区域方面表现出卓越的性能.
  • 该方法在基准数据集上实现了99.53%的高精度.
  • 与其他现有方法相比,模拟分析证实了DTLSS-MIA技术的有效性.

结论:

  • 拟议的DTLSS-MIA技术为使用MRI进行脑瘤细分提供了有效和准确的解决方案.
  • 这一进步可以显著帮助放射科医生在早期诊断和治疗规划,可能挽救生命.
  • 深度转移学习,语义细分和优化算法的集成显示了医疗图像分析的前景.