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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.
  • Software

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. Researchers at the NIHCC seek licensing of the PACS.
  • Software

Progressive and Multipath Neural Network for 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, which is available for licensing or co-development.
  • Software

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.
  • Diagnostics
  • Software

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, and this technology is available for licensing.
  • Software