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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

机器学习和深度学习用于脑瘤MRI图像细分.

Md Kamrul Hasan Khan¹, Wenjing Guo¹, Jie Liu¹

¹National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.

Experimental biology and medicine (Maywood, N.J.)

|December 16, 2023

概括

使用磁共振成像 (MRI) 精确的脑瘤细分至关重要. 本综述涵盖了机器学习和深度学习方法,强调了它们在脑瘤细分方面的优缺点. 结合技术是一个日益增长的趋势.

关键词:

机器学习机器学习大脑大脑大脑的大脑大脑深度学习是一种深度学习.图像分割图像细分图像细分磁共振成像技术的使用瘤是一个瘤.

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Last Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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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
神经瘤学神经瘤学

背景情况:

大脑瘤带来了显著的死亡风险.
精确的脑瘤细分对于患者诊断,治疗计划和疾病监测至关重要.
磁共振成像 (MRI) 是获得大脑图像的主要方式.

研究的目的:

审查用于MRI脑瘤细分的常见机器学习 (ML) 和深度学习 (DL) 技术.
讨论这些ML和DL方法的优点和局限性.
为了确定脑瘤图像分析的新兴趋势.

主要方法:

审查已建立的机器学习算法用于图像细分.
对医疗图像分析的流行深度学习架构的审查.
在脑瘤细分的背景下对ML和DL技术进行比较分析.

主要成果:

两种ML和DL方法都在脑瘤MRI细分方面表现出有效性.
每种技术都有不同的优势和局限性.
整合多种ML/DL技术是一种新兴和有前途的方法.

结论:

机器学习和深度学习是脑瘤MRI细分的重要工具.
了解个别方法的优缺点是关键.
结合多种技术的混合方法代表了改进细分精度和临床实用性的未来方向.