uOmnispace

uOmnispace is a comprehensive medical image post-processing software designed to enhance radiological workflows by providing tools for multi-modality image viewing, manipulation, communication, and storage. Its methodology leverages a client-server architecture for remote access and incorporates advanced visualization techniques, including machine learning-based segmentation for features like rib extraction, to support clinical analysis by trained professionals.

uOmnispace is a medical image post-processing software designed for viewing, manipulating, communicating, and storing medical images from multiple modalities including CT, MRI, PET, and DR. It operates as a software-only solution on standard PC hardware with a client-server architecture, allowing remote multi-user access for trained professionals like physicians and medical technicians. The system supports DICOM formatted images and objects, providing tools for 2D and 3D visualization, annotation, measurement, as well as image filming and reporting capabilities to assist in radiological tasks. It can be used as a standalone system or integrated with other cleared software options and multi-vendor applications. Technologically, uOmnispace offers advanced image processing features such as manual and automatic segmentation, including a machine learning-based algorithm for rib extraction. Its 3D review capabilities encompass Volume Rendering (VR) with Hyper Realistic Rendering (HRR), Multi-Planar Reconstruction (MPR), Maximum/Minimum Intensity Projection (MIP/MinIP), Curved Planar Reformation (CPR), Surface-shaded Display (SSD), and automatic bone removal. The software also provides 3D virtual endoscopy for vessels, airways, and colon. Performance validation for the ML-based rib segmentation algorithm on 60 chest CT scans demonstrated an average DICE score of 0.855, meeting the acceptance criterion of >0.8 and showing consistent performance across different patient subgroups. The system is not intended for displaying digital mammography images for diagnostic purposes in the U.S.

AI Regulatory Concerns

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• Q1. What is the computational performance and scalability of the uOmnispace system when handling large datasets or concurrent multiple users, particularly for its advanced 3D rendering and machine learning-based segmentation features?; How does the "Hyper Realistic Rendering (HRR)" feature specifically enhance diagnostic interpretation compared to standard Volume Rendering, and are there comparative studies validating its clinical benefit?; What are the specific interface requirements for plugging in multi-vendor applications, and how is compatibility and data integrity ensured with these third-party tools?; Beyond rib segmentation, what other anatomical structures or pathologies is the machine learning-based segmentation module being trained or planned for, and what is the development roadmap for these AI-driven tools?; Could you provide more details on the "standard annotation process" for the ground truth data used in the ML algorithm validation, particularly regarding inter-rater variability and the criteria used by the senior clinical specialist for final approval?