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.
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.
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.
Available for licensing 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.
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.
Researchers at the National Institute of Health (NIH) and National Institute of Standards and Technology (NIST) seek licensing or co-development partners for a method to predict functions, identity, disease state, and health of stem cells using machine learning.
The National Institute of Health - Clinical Center (NIH-CC) seeks licensing and/or co-development of a system and method for tracking eye movement to increase the efficacy of visual diagnoses by radiologists.
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. Researchers at the NIHCC seek licensing of the PACS.
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.
Researchers at the National Institute on Drug Abuse (NIDA) seek licensing and/or co-development research collaborations to further develop, evaluate or commercialize the software, Mobile Personalized Assessment & Learning for Addiction Treatment and Behavioral Modification. NIDA researchers developed this software for use in treating substance use disorders (drugs, alcohol, smoking) that provides personalized feedback to users.
The National Cancer Institute (NCI) seeks research, co-development, or licensing partners for software that uses computational approaches in cancer diagnosis. NCI researchers have recently developed a computational approach for detecting, quantifying, and mapping Mitotic Hotspots in whole slide images of tumor tissue. This technology has demonstrated high reproducibility that is unaffected by diagnostic skill or fatigue, allowing standardization of tumor cell proliferation assessment across institutions.
National Cancer Institute (NCI) researchers have developed a novel software tool for uniform recording of Mitotic Figure (MF) counts via conventional and/or digital microscopy. With this technology, diagnostic centers can standardize electronic recording, summation, and transcription of clinical data during surgical pathology examination. NCI seeks licensing partners to further develop this application for use in diagnosis and detection of malignant cancers.
The National Institute on Drug Abuse (NIDA) seeks licensing and/or co-development research collaborations for use of software for substance use disorders, behavior modification, and cancer patient care and pain management, etc. NIDA has developed software that permits real-time communication of patient-reported data and associated geolocation data. The software can be used in patient treatment or as a research tool for evaluating effectiveness of treatments.
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, and this technology is available for licensing.
The National Institutes of Health - Clinical Center (NIH-CC) seeks to license and/or co-develop methods of reading chest x-rays using a deep learning models to detect a disease and describe its contents.
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, which is available for licensing or co-development.
Scientists at The National Cancer Institute (NCI) and The National Institute of Neurological Disorders and Stroke (NINDS) have invented a method of imaging glucose metabolism in vivo using MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. This new technology is denoising software for MRIs that significantly improves the measurement of low-intensity signals without the need for dynamic nuclear polarization (DNP). The scientists seek research co-development partners and/or licensees for their invention.
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.