Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
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DOI：
10.1109/smc53654.2022.9945411
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
K. Nishimaki;Kumpei Ikuta;Yuto Onga;H. Iyatomi;K. Oishi
中科院分区：
文献类型：
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作者：
K. Nishimaki;Kumpei Ikuta;Yuto Onga;H. Iyatomi;K. Oishi
基于内容的图像检索（CBIR）系统是一种新兴的技术，支持阅读和解释医学图像。由于三维脑MR图像是高维的，降维是必要的CBIR使用机器学习技术。此外，对于一个可靠的CBIR系统，在所得到的低维表示中的每个维度必须与神经学上可解释的区域相关联。我们提出了一个本地化的变分自动编码器（Loc-VAE），提供神经解剖学解释的低维表示从3D脑MR图像的临床CBIR。Loc-VAE是基于$\beta-$VAE的附加约束，即低维表示的每个维度对应于大脑的局部区域。所提出的Loc-VAE能够获得保留疾病特征并且高度局部化的表示，即使在高维压缩比（4096：1）下。与朴素$\beta-$VAE相比，Loc-VAE获得的低维表示将每个维度的局部性度量提高了4. 61个点，同时保持了可比较的大脑重建能力和关于阿尔茨海默病诊断的信息。
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.