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Omnia Communications

Founded Year

1997

Stage

Acquired | Acquired

Total Raised

$18.25M

Valuation

$0000 

About Omnia Communications

Small and open intraoperative real-time MRI system for neurological surgeries

Headquarters Location

100 Nickerson Road

Marlborough, Massachusetts, 01752,

United States

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Omnia Communications Frequently Asked Questions (FAQ)

  • When was Omnia Communications founded?

    Omnia Communications was founded in 1997.

  • Where is Omnia Communications's headquarters?

    Omnia Communications's headquarters is located at 100 Nickerson Road, Marlborough.

  • What is Omnia Communications's latest funding round?

    Omnia Communications's latest funding round is Acquired.

  • How much did Omnia Communications raise?

    Omnia Communications raised a total of $18.25M.

  • Who are the investors of Omnia Communications?

    Investors of Omnia Communications include Ciena, Bessemer Venture Partners, Atlas Venture, Star Ventures and CRV.

  • Who are Omnia Communications's competitors?

    Competitors of Omnia Communications include Quality Electrodynamics, Intellirod Spine, Ortho Kinematics, Tursiop Technologies, Kjaya and 8 more.

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Kjaya is a company that received a SBIR Phase I grant for a project entitled: Semi-Autonomous Adaptive Neural and Genetic Segmentation of Medical Images. Their Phase I project will implement a physician-assisted, real-time adaptive system for the segmentation of anatomical structures in 3D medical image data. Medical image segmentation seeks to change the representation of an anatomical structure, making it more easily analyzed. Because of the extreme variability of these structures in biological systems, current idiosyncratic manual methods currently in use are tedious, time consuming, and error prone. Image segmentation cannot in general be programmatically solved. The proposed system is a Neural Network (NN) based adaptation of the individual data using parallel Graphics Processing Units (GPUs) and coupled with a Genetic Algorithm (GA) based adaptation across GPU cores. The system will build a diagnostically useful segmentation of the anatomical feature within seconds from an area of interest outlined by a physician using a Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan. Fast growth in medical imaging overwhelms available diagnosticians. An intuitive and inexpensive system to quickly and accurately deliver diagnostic relevant segmentation of medical images offers tremendous commercial value. Currently, each scan requires approximately 50 minutes of manual preparation. The diagnosis and treatment of an estimated 20 percent of diseases benefit from medical imaging. Newer scanning technologies have increased in resolution, but such techniques have not made segmenting easier or faster. The proposed method will enable more diagnostics to be done with the quality controlled directly by physicians.

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