Guided Positioning System for Ultrasound
Award last edited on: 6/26/2017

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Charles F Cadieu

Company Information

Bay Labs Inc

490 Post Street Suite 824b
San Francisco, CA 94102
   (415) 424-5616
Location: Single
Congr. District: 11
County: San Francisco

Phase I

Contract Number: 1416612
Start Date: 7/1/2014    Completed: 6/30/2015
Phase I year
Phase I Amount
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is considerable because a variety of complementary new technologies is ushering in a new era in which visual messages are becoming a first-class media type along-side text and speech. Today, both amateur and professional videographers still have to enter the virtual darkroom to sift through video, edit it, and produce engaging content. Video creation is waiting for its Polaroid moment, when a technological solution will transform the post-production time required to create engaging video. If successful, the technology developed in this project will greatly increase the utility of any video capture device and would have implications outside of Internet media in areas such as life recording and knowledge transfer. The countless video clips of important or memorable events that are today commonly archived and forgotten could instead be automatically summarized and made available in a usable and engaging format. This Small Business Innovation Research (SBIR) Phase I project aims to evaluate the technical viability of an automatic video summarization system based on neural networks and adapted to measurements of human psychology. As people collectively record more videos than they can possibly consume (the video deluge problem), a technology that automatically turns raw videos into relevant and engaging summaries becomes increasingly critical. The company's proposed platform would streamline video sharing, search, and viewing, all of which are staples of our online lives. Scientifically we are at a unique time in the capabilities of artificial visual systems, with some systems rivaling human performance in limited domains. Furthermore, the field of visual psychology has also seen recent progress in relating visual semantic information to cognitive phenomena, like memorability of images. Taken together, it may now be possible to automatically predict the cognitive relevance of visual information and produce effective video summarizations. This project combines deep neural networks for visual object recognition, recurrent networks for contextually embedded temporal information, and user measurement of interest, memorability, and uniqueness. The primary technical objective is to determine whether a system can automatically predict human-produced video summarizations.

Phase II

Contract Number: 1556103
Start Date: 4/15/2016    Completed: 3/31/2018
Phase II year
(last award dollars: 2018)
Phase II Amount

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be in the field of healthcare. The United States spends approximately $9,000 per person per year on healthcare. Ultrasound medical imaging is a medical imaging technology that could lower costs by providing an alternative to higher-cost imaging techniques. The technology created during this Phase II project is expected to increase the quality, value, and accessibility of medical ultrasound, which would in turn reduce medical imaging costs in the US healthcare system. Furthermore, the company's technology is expected to bring ultrasound to more clinical settings and improve system-wide efficiencies in the diagnosis and treatment of disease. The technology also has commercial potential in the international market, with $5.8B spent annually on medical ultrasound devices worldwide. Finally, by improving the utility of ultrasound, the technology will lead to improved patient care and may ultimately save lives.This Small Business Innovation Research (SBIR) Phase II project will develop deep learning technology for ultrasound imaging in medicine. Ultrasound imaging has numerous benefits including real-time image acquisition, non-invasive scanning, low-cost devices, and no known side-effects (it is non-ionizing). However, variability in quality has encumbered its adoption and utility. As a result, more expensive imaging is typically utilized, often exposing patients to ionizing radiation. Our objective is to develop, improve, and test machine learning techniques, based on deep learning, to improve ultrasound acquisition and interpretation. We expect this project will create novel technologies that make ultrasound easier to use and improve the quality of ultrasound examinations. The end result will improve the quality, value, and accessibility of medical ultrasound examinations, will result in cost savings to the healthcare system, will produce improvements in patient care, and will support a sustainable business opportunity.