Automatic Classification Software for MRI brain scans: A Diagnostic tool

人类面临的一个主要挑战是全球人口老龄化和相关的退化和衰弱性疾病，如阿尔茨海默病（AD）的增加。早期诊断对于提高患者生活质量和最大限度地减少社会成本至关重要。磁共振成像（MRI）是研究大脑异常的成熟工具。常规MRI只能揭示AD中的非特异性脑萎缩，但已经证明“定量”MRI技术与功能性MRI（fMRI）一起可以提供痴呆发作的特征（Bozzali et al.，2011年）。静息状态fMRI是检测静息时自发脑活动的相对新颖的方法（Greicius等人，2004年）。静息状态fMRI对于痴呆的早期诊断可能非常有效（Zhou et al.，2010年）。然而，由于数据量大、图像分析复杂、结果难以解释等原因，该方法被认为不适合临床应用。然而，原始图像甚至压缩伊卡数据的解释需要有经验的人眼。特别是，一旦网络被确定，通常使用单变量统计方法对其进行单独分析。一种提供快速、自动、客观的图像分类和诊断的多变量方法将在临床和研究领域产生巨大影响。我们的建议是说明的概念证明的自动分类的MRI成像的大脑。我们将使用现有的伊卡分解的患者和对照组在休息条件下。我们的计划是使用机器学习技术，特别是使用高斯混合的贝叶斯分类器，它已用于天文学研究。我们将编码一个原型诊断工具，基于静息状态功能磁共振成像，可以由CISC组进行测试。一旦验证，这种方法可以很容易地扩展到其他MRI models.This提案汇集了天文学中心和临床成像科学中心（CISC）在苏塞克斯大学的联合分析。天文学中心带来了统计分析和软件开发方面的广泛专业知识，而CISC带来了广泛的人类分类数据集，以及对静息状态fMRI和其他MRI技术的临床问题和专业知识的深刻理解。

A major challenge facing humankind is an aging global population and the associated increase in degenerating and debilitating diseases, such as Alzheimer's disease (AD). Early diagnosis is essential to improve patient quality of life and minimize social costs. Magnetic Resonance Imaging (MRI) is a well-established tool for studying brain abnormalities. Conventional MRI can only reveal unspecific brain atrophy in AD, but it has been demonstrated that "quantitative" MRI techniques, together with functional MRI (fMRI) can provide signatures of the onset of dementia (Bozzali et al., 2011). Resting-state fMRI is a relatively novel approach to detect spontaneous brain activity at rest (Greicius et al., 2004). Resting-state fMRI is potentially very powerful for the early diagnosis of dementia (Zhou et al., 2010). However, it is regarded as unsuitable for clinical use owing to the volume of data, the complex image analysis required, and the difficulty in the interpretation of results.A number of functional networks in the brain have been identified using Independent Component Analysis (ICA) deconstructions of resting-state fMRI data. However, the interpretation of the raw images and even the compressed ICA data requires an experienced human eye. In particular, once the networks have been identified, they are typically analysed separately using univariate statistical approaches. A multivariate approach providing a quick, automatic, objective classification and diagnosis of images would have a huge impact in clinical and research arenas. Our proposal is to illustrate proof of concept for the automatic classification of MRI imaging of the brain. We will use existing ICA decomposition of patient and control groups in resting conditions. Our plan is to use a machine learning technique, specifically a Bayesian classifier using Gaussian mixtures, which has been used in Astronomical research. We will code a prototype diagnostic tool, based on resting state fMRI, which can be tested by the CISC group. Once validated, this approach can be easily extended to the joint analysis of other MRI modalities.This proposal brings together the Astronomy Centre and the Clinical Imaging Science Centre (CISC) at the University of Sussex. The Astronomy Centre brings extensive expertise in statistical analysis and software development while the CISC brings extensive human-classified data sets and deep understanding of the clinical problems and expertise in resting state fMRI and other MRI techniques.