Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval
Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval
复制标题
使用位置感知图对组织病理学整个幻灯片图像进行编码，以进行诊断相关区域检索
DOI：
10.1016/j.media.2021.102308
复制
发表时间：
2022
影响因子：
10.9
通讯作者：
Xue Chenghai
中科院分区：
文献类型：
--
作者：
Zheng Yushan;Jiang Zhiguo;Shi Jun;Xie Fengying;Zhang Haopeng;Luo Wei;Hu Dingyi;Sun Shujiao;Jiang Zhongmin;Xue Chenghai
基于内容的组织病理学图像检索（CBHIR）是近年来组织病理学图像分析领域的一个研究热点。CBHIR系统通过从预先建立的数据库中搜索并返回与感兴趣区域（ROI）内容相似的区域，为病理学家提供辅助诊断信息。从组织病理学全切片图像数据库中检索与诊断相关的区域是一个具有挑战性且在临床应用中具有重要意义的问题。在本文中，我们提出了一个新的框架，从WSI数据库的位置感知图和深度哈希技术的基础上区域检索。与现有的CBHIR框架相比，WSI中ROI的结构信息和全局位置信息均通过图卷积和自注意操作得以保留，使得检索框架对组织分布相似的区域更加敏感。此外，得益于图的结构，所提出的框架具有良好的可扩展性的大小和形状变化的ROI。它允许病理学家根据组织的外观使用自由曲线定义查询区域。第三，基于散列技术实现检索，保证了该框架的高效性和适用性，适用于实际的大型WSI数据库。在具有2650个WSI的内部子宫内膜数据集和公共ACDC-LungHP数据集上评估了所提出的方法。实验结果表明，该方法在子宫内膜数据集和ACDC-LungHP数据集上的不规则区域检索平均精度分别达到了0.667和0.869以上，优于现有方法的上级性能。在包含1855个WSI的数据库中，平均检索时间为0.752 ms。
Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752 ms.