Our Algorithmics division uses computer vision technology to automate many of the tasks performed by clinicians interacting with volumetric medical data. We are recruiting the sharpest, most experienced, algorithmic minds available, and at last count we had a total of over 75 years of combined experience developing volumetric medical imaging algorithms. Their knowledge covers many key technologies including segmentation, registration, rendering, automated detection (CAD), vessel analysis and even volumetric CT reconstruction. Our engineers led many industry innovations and hold more than 25 patents issued or pending. We like to think of them as the industry’s “A-team” (where the A stands for either automation or algorithmic excellence).
The A-team is developing Withinsight, our advanced medical visualization and analysis software code base. Withinsight consists of a growing collection of solutions that are packaged as algorithmic “engines” licensed by other companies developing end-user clinical solutions. The engines share a framework of reusable algorithmic components named the Withinsight Framework (WIF). WIF encapsulates a wide range of efficient methods for processing and visualizing large volumetric data sets generated by medical scanners, such as CT and MRI. As the capabilities of WIF expand, we continue to create more sophisticated, versatile and robust algorithmic engines with less effort.
The types of tasks performed by Withinsight “engines” include:
Fully automated segmentation: Tagging of regions in volumetric (3D and 4D) data with anatomical labels (tissues and organs). Adding geometrical descriptors, such as centerlines, branching trees, lumen boundaries, anatomical landmarks, analytical surfaces, etc.
Elastic registration: Automatically matching homologous locations in multiple scans of the same patient, or even different patients, taken at different times by the same, or even different, modality. Quantifying and predicting change in anatomic regions over time.
Abnormality detection (CAD): Detection and marking of suspected anatomical abnormalities (eg, lesions), as well as providing quantitative statistical measurements, such as diameters, size and density, of each abnormality.
Volume visualization: Generation of informative views from volumetric data. Exposing otherwise hidden structures by using transparency effects and “flattening” of complex structures.
