Iris RESTful Server and IrisTileSource: An Iris implementation for existing OpenSeaDragon viewers
Ryan Erik Landvater MD, Navin Kathawa, Mustafa Yousif MD, Ulysses Balis MD

TL;DR
This paper introduces Iris RESTful Server and IrisTileSource, enabling efficient streaming of high-resolution whole slide images in web viewers by integrating a high-performance REST API with OpenSeaDragon.
Contribution
Developed a C++ RESTful server compatible with DICOMweb WADO-RS, and integrated it with OpenSeaDragon for seamless WSI viewer support, improving performance and compatibility.
Findings
Handles over 5,000 requests per second on a Raspberry Pi-like system.
Provides median latency of 21 ms for image requests.
Enables drop-in replacement of DZI images in WSI viewers.
Abstract
The Iris File Extension (IFE) is a low overhead performance-oriented whole slide image (WSI) file format designed to improve the image rendering experience for pathologists and simplify image management for system administrators. However, static hypertext transfer protocol (HTTP) file servers cannot natively stream subregions of high-resolution image files, such as the IFE. The majority of contemporary WSI viewer systems are designed as browser-based web applications and leverage OpenSeaDragon as the tile-based rendering framework. These systems convert WSI files to Deep Zoom Images (DZI) for compatibility with simple static HTTP file servers. To address this limitation, we have developed the Iris RESTful Server, a low-overhead HTTP server with a RESTful API that is natively compatible with the DICOMweb WADO-RS API. Written in C++ with Boost Beast HTTP and Asio networking libraries atop…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Radiography and Breast Imaging · Cloud Computing and Remote Desktop Technologies · Cell Image Analysis Techniques
