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OpenDicomViewer: A Lightweight Open-Source DICOM Viewer for macOS Built with Swift
JoonNyung Heo1,2, Daeseong Kim3, Kunhee Kim4
1Department of Neurology, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemoon-Gu 03722, Seoul, Korea. jnheo@yuhs.ac.
Journal of Imaging Informatics in Medicine
|June 30, 2026
Summary
OpenDicomViewer (ODV), a new open-source DICOM viewer for macOS, uses Swift and SwiftUI for efficient local image viewing. ODV demonstrates significantly lower memory consumption compared to established viewers like Horos and OsiriX MD.
Area of Science:
- Medical Imaging Technology
- Software Engineering
- Open-Source Development
Background:
- Established open-source macOS DICOM viewers like Horos utilize legacy codebases (Objective-C/C++).
- Large source code lines in legacy viewers may hinder maintainability and extensibility.
- There is a need for modern, efficient, and lightweight DICOM viewers for macOS.
Purpose of the Study:
- To develop and evaluate OpenDicomViewer (ODV), a novel, open-source DICOM viewer for macOS.
- To build ODV using modern Swift and SwiftUI for a native macOS experience.
- To assess ODV's performance, particularly memory footprint, against existing viewers.
Main Methods:
- Developed ODV as a native macOS application entirely in Swift and SwiftUI.
- Implemented features including multi-panel layouts, MPR, and raycasting.
- Compared ODV's peak memory footprint against Horos and OsiriX MD using the Pancreas-CT dataset on a Mac Studio.
Main Results:
- ODV, with 12,952 lines of Swift code, consumed 9.6 GB peak memory.
- Horos (30.0 GB) and OsiriX MD (23.6 GB) showed significantly higher memory consumption.
- ODV's codebase is substantially smaller (23.9x fewer lines than Horos main app directory).
Conclusions:
- ODV offers efficient local DICOM viewing with lower memory usage than Horos and OsiriX MD.
- A Swift-native approach provides a modern, compact codebase for easier maintenance and potential AI integration.
- ODV represents a viable, lightweight alternative for macOS-based DICOM image viewing.
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