Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging
Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging
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DOI：
10.1016/j.media.2021.102227
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发表时间：
2021-09-17
影响因子：
10.9
通讯作者：
Hamamoto, Ryuji
中科院分区：
文献类型：
--
作者：
Kobayashi, Kazuma;Hataya, Ryuichiro;Hamamoto, Ryuji
在医学影像学中，纯粹来自疾病的特征应该反映异常发现偏离正常特征的程度。实际上，医生通常需要没有感兴趣的异常发现的对应图像，或者相反地，包含类似异常发现的图像，而不管正常解剖背景。这被称为医学图像的比较诊断阅读，这对于正确的诊断是必不可少的。为了支持比较诊断阅读，基于内容的图像检索（CBIR），可以选择性地利用正常和异常功能的医学图像作为两个可分离的语义成分将是有用的。在这项研究中，我们提出了一个神经网络架构，医学图像的语义成分分解成两个潜在的代码：正常解剖代码和异常解剖代码。正常解剖结构代码表示如果样本健康则应该存在的反事实正常解剖结构，而异常解剖结构代码归因于反映偏离正常基线的异常变化。通过计算基于正常或异常解剖代码或两个代码的组合的相似性，我们的算法可以检索图像根据选定的语义组件从数据集组成的脑胶质瘤的磁共振图像。此外，它可以利用一个合成的查询向量结合正常和异常的解剖代码从两个不同的查询图像。为了评估检索到的图像是否是根据目标语义分量获取的，计算地面实况标签的重叠作为语义一致性的度量。我们的算法提供了一个灵活的CBIR框架，通过处理的分解功能与定性和定量显着的结果。(c)2021作者（S）由Elsevier B.V.发布。这是CC BY许可下的开放获取文章（http://creativecommons.org/licenses/by/4.0/）
In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code . The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )