Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. Loc-VAE is based on $\beta$-VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $\beta$-VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer's disease.
翻译:基于内容的图像检索系统(CBIR)是一种支持阅读和解读医学图像的新兴技术。由于3D脑MR图像是高维的,因此使用机器学习技术对CBIR而言必须减少维度。此外,对于可靠的CBIR系统来说,由此产生的低维表示器的每个层面都必须与神经学上可解释的区域相联系。我们建议使用一个局部变异自动显示器(Loc-VAE),为临床CBIR提供3D脑MR图像的可神经解析的低维度表示器。Loc-VAE基于$\beta$-VAE,而低维度表示器的每个层面又受到额外的限制,即低维度表示器的每个层面都与大脑的某一区域相对应。提议的Loc-VAE能够获得保持保留疾病特征和高度局部化的表示器,即使处于高维度压缩比率(4096:1) 。Loc-VAE的低维代表器将每种层面的局部度表示器测测度测量点比天时增加了4.61点,同时保持可比较的大脑重建能力和关于阿尔茨海默症的诊断的信息。