Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images

TL;DR

Yottixel is a specialized image search engine for large histopathology archives that efficiently indexes whole slide images using compact barcodes, enabling real-time, accurate retrieval of similar images for diagnostic support.

Contribution

The paper introduces Yottixel, a novel search engine that uses a unique barcode-based indexing method to enable fast, accurate, and storage-efficient retrieval of large-scale histopathology images.

Findings

Yottixel can perform millions of searches in real-time.

It accurately retrieves images of organs and malignancies.

Semantic ordering aligns well with human judgment.

Abstract

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a portmanteau for "one yotta pixel," alluding to the big-data nature of histopathology images. The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. Yottixel can perform millions of searches in real-time with a high search accuracy and low storage profile. Yottixel uses an intelligent indexing algorithm capable of representing WSIs with a mosaic of patches by converting them into a small number of methodically extracted barcodes, called "Bunch of Barcodes" (BoB), the most prominent performance enabler of Yottixel. The performance of the prototype platform is qualitatively tested using 300 WSIs from the University of Pittsburgh Medical Center (UPMC) and 2,020 WSIs from The Cancer Genome Atlas Program (TCGA) provided by the National Cancer Institute. Both datasets amount to more than 4,000,000 patches of 1000x1000 pixels. We report three sets of experiments that show that Yottixel can accurately retrieve organs and malignancies, and its semantic ordering shows good agreement with the subjective evaluation of human observers.