Technology | Advanced Visualization | November 30, 2015

Fovia Introduces F.A.S.T. Interactive Segmentation

Software offers dynamic 3-D interactive segmentation with real-time results

November 30, 2015 — Fovia Inc. introduced F.A.S.T. Interactive Segmentation at the 2015 Radiological Society of North America (RSNA) annual meeting.  Powered by Fovia's XStream HDVR, F.A.S.T. provides dynamic, real-time, interactive 3-D segmentation. Original equipment manufacturers (OEMs) can quickly create rich visualization applications based on F.A.S.T. Interactive Segmentation in areas such as quantitative imaging, radiation therapy, surgical planning, robotic surgery, 3-D printing and more.

The platform easily overcomes common segmentation pain points and time-consuming processes.  Previously, radiologists typically segmented regions of interest manually — which could take an hour or more — or relied on automatic segmentation algorithms, which are not failsafe, leaving radiologists with the time-consuming task of correcting automatic segmentations in 2-D, slice-by-slice—or ignoring the results altogether.

F.A.S.T. Interactive 3-D Segmentation provides a complete and intuitive interactive experience, enabling a user to quickly and easily segment (and modify on-the-fly if necessary) anatomic regions of interest. Continuous control of segmentation parameters and access to instantaneous results via high fidelity 3-D rendering allow cases to be accurately segmented, adjusted and analyzed in seconds. Additionally, the platform is designed to complement existing automatic segmentation algorithms, allowing rapid, real-time clean-up of data to meet radiologists' exacting requirements.

Whether used alone or in conjunction with other segmentation algorithms, F.A.S.T. Interactive Segmentation allows physicians to reduce complex case times from hours to seconds, thereby expediting reporting and streamlining diagnoses. It provides comprehensive interactive feedback of dynamic segmentation results through strategic use of Fovia's proprietary high performance algorithms. 

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