The National Eye Institute (NEI) seeks research co-development partners and/or licensees for an automatic deep learning-based algorithm to detect and quantitate ellipsoid zone (EZ) loss in Spectral Domain Optical Coherence Tomography (SD-OCT) images.
This technology includes a fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.
This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.
Developing computer systems that can recognize and locate image features associated with disease is a challenge for developing fully-automated and high precision computer assisted diagnostics. Joint learning of language tasks in association with vision tasks (association of image features with text annotation) adds an additional level of challenge. Furthermore, scaling-up approaches from small to large datasets presents additional issues, particularly related to medical images.
Multiparametric MRI improves image detail and prostate cancer detection rates compared to standard MRI. Computer aided diagnostics (CAD) used in combination with multiparametric MRI images may further improve prostate cancer detection and visualization. The technology, developed by researchers at the National Institutes of Health Clinical Center (NIHCC), is an automated CAD system for use in processing and visualizing prostate lesions on multiparametric MRI images.
The technology relates to software, systems, and methods for automated medical image segmentation via deep learning. Standard holistically-nested networks (HNNs) used in computer vision and medical imaging for segmentation and edge detection have a problem with coarsening resolution. The described technology addresses this.
Medical image datasets are an important clinical resource. Effectively referencing patient images against similar related images and case histories can inform and produce better treatment outcomes. Labeling and identifying disease features and relations between images within a large image database has not been a task capable of automation. Rather, it is a task that must be performed by highly trained clinicians who can identify and label the medically meaningful image features.
Accurate automated organ and disease feature segmentation is a challenge for medical imaging analysis. The pancreas, for example, is a small, soft, organ with low uniformity of shape and volume between patients. Because of the lack of uniform image patterns, there are few features that can be used to aid in automated identification of anatomy and boundaries. Segmentation of high variability features is uniquely difficult for a computer to perform.
Accurate automated organ and disease feature segmentation is a challenge for medical imaging analysis. The pancreas, for example, is a small, soft, organ with low uniformity of shape and volume between patients. Because of the lack of uniform image patterns, there are few features that can be used to aid in automated identification of anatomy and boundaries. Segmentation of high variability features is uniquely difficult for a computer to perform.