Luigi: Large-scale histopathological image retrieval system using deep texture representations

Background: As a large number of digital histopathological images have been accumulated, there is a growing demand of content-based image retrieval (CBIR) in pathology for educational, diagnostic, or research purposes. However, no CBIR systems in digital pathology are publicly available. Results: We developed a web application, the Luigi system, which retrieves similar histopathological images from various cancer cases. Using deep texture representations computed with a pre-trained convolutional neural network as an image feature in conjunction with an approximate nearest neighbor search method, the Luigi system provides fast and accurate results for any type of tissue or cell without the need for further training. In addition, users can easily submit query images of an appropriate scale into the Luigi system and view the retrieved results using our smartphone application. The cases stored in the Luigi database are obtained from The Cancer Genome Atlas with rich clinical, pathological, and molecular information. We tested the Luigi system and the smartphone application by querying typical cancerous regions from four cancer types, and confirmed successful retrieval of relevant images with both applications. Conclusions: The Luigi system will help students, pathologists, and researchers easily retrieve histopathological images of various cancers similar to those of the query image. Luigi is freely available at https://luigi-pathology.com/.