You are here

Share:

Search Technologies

Showing 1-16 of 16 results found

A Secure, Web-Based Biorepository Database

The National Cancer Institute seeks collaborators to co-develop a secure, web-based system that manages multi-dimensional data models on biospecimens.

A Mobile Health Platform

Researchers at the National Institute on Drug Abuse (NIDA) seek licensing or co-development of a mobile health technology that monitors and predicts a user’s psychological status in order to deliver an automated intervention when needed.

Software for Accurate Segmentation of Cell Nuclei in Breast Tissue

The Office of the Director, National Cancer Institute is seeking statements of capability or interest from parties interested in collaborative research (using the Cooperative Research and Development Agreement (CRADA) or Material Transfer Agreement (MTA) to further develop, evaluate, or commercialize the software for accurate segmentation of cell nuclei and FISH signals in tissue sections. Collaborators working in the field of quantitative and automated pathology may be interested.

Software for Automated Generation of Density Maps

The technology available for licensing or co-development is computer software for the automated generation of density maps of macromolecular structures from a series of 2D digital micrographs of frozen hydrated specimens collected using an electron microscope equipped with an ultra-cooled computerized stage.

Web Application for Managing Electronic Health Records

This software can standardize and manage the process by which order sets are developed, less error-prone and more timely stages of an order set request with clinical and organizational staff become the norm.

Subject Matter Expertise Reference System (SMERS)

The National Institute on Drug Abuse (NIDA) is seeking statements of capability or interest from parties interested in collaborative research to further develop, evaluate, or commercialize a search engine for PubMed and other information warehouses. As a Research Tool, patent protection is not being pursued for this technology.

Video Monitoring and Analysis System for Vivarium Cage Racks

This invention pertains to a system for continuous observation of rodents in home-cage environments with the specific aim to facilitate the quantification of activity levels and behavioral patterns for mice housed in a commercial ventilated cage rack.  The National Cancer Institute’s Radiation Biology Branch seeks partners interested in collaborative research to co-develop a video monitoring system for laboratory animals.

3D Image Rendering Softwarefor Biological Tissues

The Frederick National Laboratory for Cancer Research seeks parties interested in collaborative research to co-develop software for the automatic 3-D visualization of biological image volumes.

Human Research Information System (HuRIS)

Researchers at the National Institute on Drug Abuse (NIDA) seek licensing or co-development of a Human Research Information System (HuRIS) software that automates all major functions of a clinical-research entity. The system is designed for commercial healthcare providers, community treatment centers, and clinical research facilities.

Optical Microscope Software for Breast Cancer Diagnosis

Researchers from NCI and Rudgers University developed  methods of detecting abnormal cells in a sample using the spatial position of one or more genes within the nucleus of a cell, as well as a kit for detecting abnormal cells using such methods. The invention also provides methods of identifying gene markers for abnormal cells using the spatial position of one or more genes within the nucleus of a cell. The National Cancer Institute seeks parties interested in collaborative research to co-develop diagnostic methods for detection of cancer using spatial genome organization.

Method and System of Building Hospital-Scale Medical Image Database

Hospital Picture Archiving and Communication Systems (PACS) contain vast amounts of underutilized informatics about disease conditions. As computer image processing and systems advance, PACS informatics may form the foundation for precision automated computer-aided diagnostics for a wide range of disease conditions. Development of such systems may improve diagnostic accuracy and better inform treatment, but creating systems and algorithms capable of “learning” to recognize and locate the image patterns of disease and associated labels is a difficult problem. Researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that applies deep learning to PACS images to produce a database where certain disease features are identified and spatially located.

Computer Aided Diagnostic for use in Multiparametric MRI for Prostate Cancer

Researchers at the National Institutes for Health Clinical Center (NIHCC) have developed computer-aided diagnostics (CAD) that may further improve the already superior capabilities of multiparametric magnetic resonance imaging (MRI) for detection and imaging of prostate cancer. This system produces an accurate probability map of potential cancerous lesions in multiparametric MRI images that is superior to other systems and may have multiple product applications.

Convolutional Neural Networks for Organ Segmentation

Computer automated segmentation of high variability organs and disease features in medical images is uniquely difficult. The application of deep learning and specialized neural networks may allow for automation of such interpretation tasks that are currently only performed by trained physicians. Computer automation may improve image analysis capabilities and lead to better diagnostics, disease monitoring, and surgical planning for many diseases. To help solve this challenge, researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that trains a computer to read and segment certain highly variable image features.

Convolutional Neural Networks for Organ Segmentation

Computer automated segmentation of high variability organs and disease features in medical images is uniquely difficult. The application of deep learning and specialized neural networks may allow for automation of such interpretation tasks that are currently only performed by trained physicians. Computer automation may improve image analysis capabilities and lead to better diagnostics, disease monitoring, and surgical planning for many diseases. To help solve this challenge, researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that trains a computer to read and segment certain highly variable image features.

Progressive and Multipath Holistically Nested Neural Network for Improved Detail Level in Medical Image Segmentation

Researchers at the National Institutes of Health Clinical Center (NIHCC) developed a technology that improves segmentation detail levels for anatomical structures in medical images through a new, deep learning approach. Difficult anatomical features, often segmented incorrectly with other image segmentation methods, are correctly segmented and identified using this novel technology.