MIST: Medical Image Streaming Toolkit
A unified framework for intelligent, progressive, and resource-efficient medical image streaming.
MIST and ISLE represent two complementary frameworks addressing the challenges of large-scale medical imaging datasets and AI-driven inference systems.
Overview
MIST: Medical Imaging Streaming Toolkit
- Challenge: Large-scale imaging datasets require significant storage and bandwidth, limiting accessibility for AI research and clinical deployment.
- MIST Solution: A format-agnostic database enabling streaming of medical images at multiple resolutions and formats from a single high-resolution copy.
- Evaluation: Tested across eight diverse datasets (CT, MRI, X-ray) covering multiple modalities and file formats.
- Results: Reduced storage and bandwidth requirements without impacting image quality or downstream deep learning performance.
- Impact: Creates a data-efficient, format-agnostic platform that reduces barriers to AI research in medical imaging.
ISLE: Intelligent Streaming for AI Inference
- Motivation: Growing adoption of AI systems in radiology is increasing demands for bandwidth and computational resources.
- ISLE Framework: An intelligent streaming method inspired by video-on-demand platforms to deliver only the resolution needed for AI inference using progressive encoding.
- Results (Classification): Reduced transmission by ≥90% and decoding time by ≥87%
- Results (Segmentation): Reduced transmission by ≥77% and decoding time by ≥89%
- Performance: No impact on diagnostic performance (all P > 0.05).
- Impact: Improves data and computational efficiency for AI deployment in clinical environments without compromising diagnostic accuracy.
Open-Source Tools
| Component | Description | Repository |
|---|---|---|
| MIST | Core streaming and dataset management framework | GitHub |
| IntelligentStreaming | AI-aware streaming for real-time inference | GitHub |
| OpenJPHpy | Python interface for HTJ2K codec | GitHub |
Patents
Patent: WO2024233969A1 — Systems and methods for high-throughput analysis for graphical data
Filed by: University of Maryland Baltimore
Inventors: Vishwa S. Parekh, Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Eliot L. Siegel
Publications
Kulkarni P., Kanhere A., Siegel E.L., Yi P.H., Parekh V.S. ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging. Journal of Imaging Informatics in Medicine. 2024 Dec;37(6):3250-63. DOI
Kulkarni P., Kanhere A., Siegel E., Yi P., Parekh V.S. Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning. Medical Imaging with Deep Learning (MIDL) (2025). OpenReview
Contact
Dr. Vishwa S. Parekh
UTHealth Houston
vishwa.s.parekh@uth.tmc.edu