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Brain Abscess l: Introduction

Brain Abscess l: Introduction

A brain abscess is a focal, intracerebral infection characterized by a localized collection of pus within the brain parenchyma, resulting from microbial invasion and the body’s inflammatory response. It progresses through stages: early and late cerebritis, followed by early and late capsule formation, reflecting tissue destruction, immune response, and eventual encapsulation.Etiology and PathogenesisCausative organisms vary with source and host factors, often involving polymicrobial infections,...

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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在没有扫描器特定的正常数据库的单个受试者中,使用卷积神经网络进行基于voxel的形态测量.

Julia Krüger¹, Roland Opfer¹, Lothar Spies¹

¹jung diagnostics GmbH, Hamburg, Germany.

European radiology

|November 9, 2023

概括

此摘要是机器生成的。

一个新的基于voxel的形态测量 (VBM) 的深度卷积神经网络 (CNN) 可以在不需要扫描器特定的正常数据库的情况下检测个体的脑缩. 这种CNN-VBM方法的性能与传统的VBM相美,有可能扩大用于神经退行性疾病诊断的临床用途.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的疾病大脑绘制地图.深度学习是一种深度学习.磁共振成像技术磁共振成像技术神经网络 (计算机)

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科学领域:

神经成像是一种神经成像.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

基于Voxel的形态测量 (VBM) 对于在MRI扫描中检测疾病特异性脑缩至关重要.
传统的VBM需要扫描器特定的正常数据库 (NDB),这限制了其广泛的临床应用.
缺乏易于获得的NDBs对VBM的常规使用构成了重大障碍.

研究的目的:

设计,训练和验证深度卷积神经网络 (CNN) 用于单个主体VBM分析.
开发一种基于CNN的VBM (CNN-VBM) 方法,可以消除对扫描器特定的NDB的需求.
评估CNN-VBM在检测与阿尔茨海默病 (AD) 和前叶退化 (FTLD) 相关的缩方面的表现.

主要方法:

一个CNN在65个扫描仪的8945 T1w MRI扫描上受过训练,使用传统的VBM与扫描仪特定的NDB用于黄金标准地图.
在对健康对照组,AD患者和FTLD患者的独立数据集上评估了CNN-VBM.
通过使用Dice系数和两个独立的读取器的视觉分类准确度来量化性能.

主要成果:

CNN-VBM地图与传统的VBM地图具有很高的相似性,其中子系数的中位数为0.85.
对视觉分类VBM地图来检测AD或FTLD的整体准确率为CNN-VBM的89.8%,传统VBM的89.0%.
CNN-VBM的性能与传统的VBM方法相美.

结论:

CNN-VBM有效地检测疾病特异性缩,而不需要扫描器特异性的正常数据库.
这种深度学习方法为传统的VBM提供了可行的替代方案,克服了NDB可用性的限制.
CNN-VBM有可能促进VBM在诊断神经退行性疾病方面的更广泛临床应用.