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.

Tailoring the best treatments to cancer patients remains a highly important endeavor in the oncology field. However, personalized treatment courses are challenging to determine, and technologies or methods that can successfully be employed for precision oncology are lacking.

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.

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.

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.

Researchers at the National Institute on Drug Abuse (NIDA) have developed software that provides personalized feedback for treating drug dependence, alcoholism, smoking cessation, pain management, and associated risky behaviors. The tool is designed for both healthcare providers at the point-of-care and for self-help. Many people who could benefit from treatment do not receive it because of its low availability and high cost.

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.

The available system is the Human Research Information System (HuRIS), an integrated advanced clinical/research informatics series of systems—that is, an intelligent electronic environment for the collection, organization and retrieval of information in clinical/scientific decision support—which enables data and resource sharing in real time among authorized users at our clinics. (Individual systems or subsystems may be licensable.)

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.