National Cancer Institute dosimetry system for Nuclear Medicine (NCINM) Computer Program

Nuclear medicine is the second largest source of medical radiation exposure to the general population after computed tomography imaging. Imaging modalities utilizing nuclear medicine produce a more detailed view of internal structure and function and are most commonly used to diagnose diseases such as heart disease, Alzheimer’s and brain disorders. They are used to visualize tumors, abscesses due to infection or abnormalities in abdominal organs.

Convolutional Neural Networks for Organ Segmentation

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.

Convolutional Neural Networks for Organ Segmentation

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.

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

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.

Eye Tracking Application in Computer Aided Diagnosis and Image Processing in Radiology

Medical imaging is an important resource for early diagnostic, detection, and effective treatment of cancers. However, the screening and review processes for radiologists have been shown to overlook a certain percentage of potentially cancerous image features. Such review errors may result in misdiagnosis and failure to identify tumors. These errors result from human fallibility, fatigue, and from the complexity of visual search required.

Software for Modeling Delivery and Penetration of Antibody Conjugates

The National Cancer Institute (NCI) seeks parties to license software for modeling the targeted delivery of anti-cancer agents in solid tumors.

The software models the permeability and concentration of intravenously administered antibody anti-cancer agent conjugates in solid tumors.  The models can be used to determine optimal dosing regimen of a therapeutic in a particular cancer type.  Thirty factors that affect delivery rates and efficiencies are analyzed as variables in generating the models.

3D Image Rendering Software for Biological Tissues

Available for commercial development is software that provides automatic visualization of features inside biological image volumes in 3D. The software provides a simple and interactive visualization for the exploration of biological datasets through dataset-specific transfer functions and direct volume rendering. The method employs a K-Means++ clustering algorithm to classify a two-dimensional histogram created from the input volume. The classification process utilizes spatial and data properties from the volume.

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.

Mitotic Figures Electronic Counting Application for Surgical Pathology

Cancer diagnosis depends on the assessment of patient biopsies to determine tumor type, grading, and stage of malignancy. Pathologists visually review specimens and count mitotic figures (MF) in a variety of cancer types to help gauge aggressiveness, guide treatment, and inform patient prognosis. Current technology for recording MF counts in surgical pathology is lacking in objectivity, and enumeration of MF by microscopy can be error prone. In particular, a lack of systematic means for recording contributes to recognized variability.

Denoising of Dynamic Magnetic Resonance Spectroscopic Imaging Using Low Rank Approximations in the Kinetic Domain

Accurate measurement of low metabolite concentrations produced by medically important enzymes is commonly obscured by noise during magnetic resonance imaging (MRI). Measuring the turnover rate of low-level metabolites can directly quantify the activity of enzymes of interest, including possible drug targets in cancer and other diseases. Noise can cause the in vivo signal to fall below the limit of detection. A variety of denoising methods have been proposed to enhance spectroscopic peaks, but still fall short for the detection of low-intensity signals.