Compare DWV (DICOM Web Viewer) vs. RedBrick AI in 2025

RedBrick AI

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Description

DWV, or DICOM Web Viewer, is an efficient and open-source tool for viewing DICOM images, developed using JavaScript and HTML5, which operates seamlessly within contemporary web browsers, eliminating the need for any extra plugins or installations. This viewer empowers users to engage with DICOM medical images through widely used web technologies, featuring capabilities such as adjusting window/level settings, zooming in and out, panning across images, and measuring distances and angles, along with inspecting DICOM tags. Additionally, DWV accommodates various image layouts and supports both multi-frame and multi-series DICOM files, ensuring comprehensive functionality. Users can annotate images with basic tools and can easily load files from local storage or via URLs, enhancing usability. The viewer's drag-and-drop feature allows for convenient file management, and its flexible architecture enables developers to embed it into tailored web applications. Actively maintained on GitHub, DWV prioritizes responsive design, ensuring optimal accessibility across a range of devices including desktops, tablets, and smartphones, making it a versatile solution for medical imaging needs. Moreover, the viewer's ongoing updates and community support contribute to its robustness and reliability in handling medical image data.

Description

RedBrick AI serves as a rapidly collaborative platform for annotating medical data, specifically designed to assist healthcare AI teams in creating high-quality training datasets across various types of radiological imagery, including CT, MRI, X-ray, Ultrasound, Fluoroscopy, and additional standard imaging techniques. The platform is adept at managing intricate tasks such as multi-series annotation and extensive DICOM studies, thanks to its native compatibility with medical data formats including DICOM and NIfTI. Furthermore, it boasts cutting-edge, user-friendly 2D and 3D web-based annotation tools, complemented by a PACS-like viewer. RedBrick AI supports a wide array of annotation use cases, including instance and semantic segmentation, landmark identification, classification, and ROI measurements, thereby enhancing the speed of annotation processes by as much as 60%. This significant improvement in efficiency can empower healthcare professionals to focus more on patient care rather than on time-consuming data preparation tasks.