Building a secure and performative DICOM viewer
Months of Development Timeline
PHI Removal Accuracy
Expert Team
Client Background
A leading neuro-oncology research team, headed by a Professor of Neuro-Oncology, required a modern medical imaging platform to support brain tumor research and longitudinal patient monitoring.
The researchers work with large MRI and CT studies stored in DICOM format and regularly perform detailed tumor measurements, image annotations, and follow-up comparisons across multiple examinations. Their long-term objective was to accelerate research workflows, improve annotation consistency, and prepare high-quality, privacy-compliant datasets for AI model development.
Success Metrics
Clear decisions. Predictable outcomes. From insight to impact — with measurable results.
Faster AI Dataset Preparation
Platform Availability
Viewer Interaction Latency
Less of Longitudinal Analysis Effort
Business Challenge
The client relied on several existing DICOM viewers, but none fully supported their research workflow. Commercial solutions lacked advanced longitudinal comparison capabilities, offered limited annotation functionality, or made anonymization and AI dataset preparation unnecessarily complex. As the research program expanded, manual processes became increasingly time-consuming and difficult to scale. The organization needed a secure, web-based DICOM viewer capable of viewing MRI and CT studies from local storage and remote DICOM repositories, performing accurate slice-by-slice tumor contouring, automatically calculating 3D tumor volumes, comparing multiple patient studies across different timepoints, removing protected health information (PHI) before data export, and preparing standardized datasets for machine learning research.
The solution also needed to maintain high performance while rendering large imaging studies directly in the browser.
The Solution
We designed and developed a browser-based DICOM Viewer specifically tailored for clinical research and medical AI initiatives. The platform combines high-performance medical image visualization with secure data processing and advanced annotation capabilities, allowing researchers to complete their entire workflow within a single application.
Case attributes
Platform
macOS, Windows
Team Composition
PM
3 Developers
QA Engineer
Location
USA
Technology stack
DICOMweb
DICOM over TCP/IP
OpenGL
VTK
DCMTK
GDCM
Qt / QML
CMake
Methodology
Agile
Technical Highlights
Building a performant browser-based DICOM viewer required solving several complex engineering challenges related to medical imaging, data processing, and secure healthcare workflows. These technical innovations enabled the platform to deliver both clinical accuracy and an exceptional user experience.
High-Performance Browser Rendering
Large MRI and CT studies containing hundreds or thousands of slices remain responsive through progressive loading, intelligent caching, and rendering optimizations.
Accurate 3D Volume Reconstruction
Voxel spacing, slice thickness, and interpolation algorithms were carefully validated to produce clinically reliable volumetric measurements.
Longitudinal Patient Comparison
The platform automatically aligns baseline and follow-up examinations, allowing researchers to accurately monitor tumor progression over time while preserving pseudonymous patient identities.
Secure Medical Data Processing
Protected health information is removed from both DICOM metadata and image overlays before any dataset leaves the platform.
Optimized DICOMweb Connectivity
Smart caching, progressive retrieval, and efficient network requests minimize loading times when accessing remote PACS and DICOMweb repositories.
The Result
The delivered solution significantly streamlined medical imaging research by consolidating visualization, annotation, anonymization, and AI dataset preparation into a single secure platform.
Researchers can now analyze longitudinal studies faster, produce more consistent tumor measurements, and generate machine learning datasets without relying on multiple disconnected tools.
Value Delivered by devPulse
Beyond delivering a feature-rich DICOM viewer, devPulse helped the client streamline medical imaging research, improve data quality, and establish a scalable foundation for future AI initiatives. The solution delivers measurable value across research productivity, security, and long-term platform evolution.
Researchers can review studies, create precise annotations, compare multiple timepoints, and calculate 3D tumor volumes within a single browser-based application.
Automated workflows and intuitive visualization tools significantly reduce the time required for image analysis, annotation, and longitudinal tumor assessment.
Built-in de-identification removes protected health information from both DICOM metadata and image overlays, enabling secure collaboration across research teams.
Standardized exports in DICOM SEG, NIfTI, and JSON formats simplify the preparation of high-quality datasets for machine learning and computer vision projects.
Version-controlled annotations, audit logs, and standardized review workflows help maintain consistent measurements and complete research traceability.
The modular architecture enables future integration with AI-assisted segmentation, cloud infrastructure, additional imaging modalities, and hospital information systems without major platform redesign.
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