相关实验视频

Updated: Jul 25, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

深度学习在使用不同MRI序列对恶性原发性和转移性脑瘤进行分类方面的表现:一项医学分析研究

Adam Fauzi¹, Yuyun Yueniwati², Agus Naba³

¹Study Program of Master in Biomedical Science, Faculty of Medicine, Universitas Brawijaya Malang, Malang, Indonesia.

Journal of X-ray science and technology

|June 25, 2023

概括

像ResNet101和VGG19这样的深度学习模型在使用MRI对恶性原发性脑瘤 (MPBT) 和转移性脑瘤 (MBT) 的分类方面显示出前景. 特定的MRI序列影响瘤检测灵敏度,有助于临床决策.

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:

恶性原发性脑瘤 (MPBT) 和转移性脑瘤 (MBT) 是常见的,需要不同的管理策略.
磁共振成像 (MRI) 对于瘤评估至关重要.
深度学习 (DL) 提供了增强MPBT和MBT分类的潜力.

研究的目的:

评估不同MRI序列对MPBT与MBT的DL分类性能的影响.
从临床角度分析分类结果.

主要方法:

从四个MRI序列收集和预处理了1360张图像.
训练并评估了VGG19和ResNet101 DL模型.
使用准确度,灵敏度和混矩阵分析评估模型性能.

主要成果:

在MPBT分类方面,ResNet101获得了83%的准确率;在MBT分类方面,VGG19获得了81%的准确率.
T2序列对MPBT显示了最高的灵敏度.
T1C和T1序列对MBT表现出最高的灵敏度.

结论:

DL模型 (ResNet101,VGG19) 显示出从MRI分类MPBT和MBT的巨大潜力.
磁力共振成像 (MRI) 序列选择影响瘤检测灵敏度.
这些发现支持DL在脑瘤分类中的进步,以改善患者的治疗结果.

关键词:

深度学习是一种深度学习.磁力共振成像 (MRI) 序列脑瘤分类大脑瘤的分类恶性原发性脑瘤是恶性脑瘤.转移性大脑瘤的发生.

更多相关视频

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Modeling Brain Metastasis by Internal Carotid Artery Injection of Cancer Cells

Modeling Brain Metastasis by Internal Carotid Artery Injection of Cancer Cells

Published on: August 2, 2022

相关概念视频

Magnetic Resonance Imaging

Magnetic Resonance Imaging

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...