Iris: A Next Generation Digital Pathology Rendering Engine
Open accessAbstract
Digital pathology is a tool of rapidly evolving importance within the discipline of pathology. Whole slide imaging promises numerous advantages; however, adoption is limited by challenges in ease of use and speed of high-quality image rendering relative to the simplicity and visual quality of glass slides. Herein, we introduce Iris, a new high-performance digital pathology rendering system. Specifically, we outline and detail the performance metrics of Iris Core, the core rendering engine technology. Iris Core comprises machine code modules written from the ground up in C++ and using Vulkan, a low-level and low-overhead cross-platform graphical processing unit application program interface, and our novel rapid tile buffering algorithms. We provide a detailed explanation of Iris Core's system architecture, including the stateless isolation of core processes, interprocess communication paradigms, and explicit synchronization paradigms that provide powerful control over the graphical processing unit. Iris Core achieves slide rendering at the sustained maximum frame rate on all tested platforms (120 FPS) and buffers an entire new slide field of view, without overlapping pixels, in 10 ms with enhanced detail in 30 ms. Further, it is able to buffer and compute high-fidelity reduction-enhancements for viewing low-power cytology with increased visual quality at a rate of 100-160 μs per slide tile, and with a cumulative median buffering rate of 1.36 GB of decompressed image data per second. This buffering rate allows for an entirely new field of view to be fully buffered and rendered in less than a single monitor refresh on a standard display, and high detail features within 2-3 monitor refresh frames. These metrics far exceed previously published specifications, beyond an order of magnitude in some contexts. The system shows no slowing with high use loads, but rather increases performance due to graphical processing unit cache control mechanisms and is "future-proof" due to near unlimited parallel scalability.
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AI implementation requires robust computational infrastructure, including high performance servers with graphic processing units (GPUs) or tensor processing units (TPUs) capable of handling gigapixel WSIs.
“AI implementation requires robust computational infrastructure, including high performance servers with graphic processing units (GPUs) or tensor processing units (TPUs) capable of handling gigapixel WSIs [77].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [77]
The Discussion (page not provided) calls for high-performance computing infrastructure with GPUs and says GPU acceleration can support AI analysis of large pathology images. The Introduction describes multi-gigapixel images, but the paper does not specify TPUs or establish that AI implementation requires high-performance servers; its reported rendering tests used a laptop. (LLM, conf=0.88)
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References 17 · Cited by 3
- Authors
- Landvater, R. E., & Balis, U.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Landvater, Ryan Erik
- ORCID
- Balis, Ulysses
- Venue
- Journal of Pathology Informatics
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 16
- Pages
- 100414
- ISSN
- 2153-3539 (ISSN-L)
- PMC
- PMC11742306
- OpenAlex
- W4405044227
- Cited by
- 3
- References
- 17
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Pathology Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- University of Michigan
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- digital pathology, whole slide imaging, Vulkan, GPU rendering, interprocess communication, synchronization, rendering performance
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