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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.

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.

The NIH Clinical Center seeks parties interested to license a method and apparatus that can significantly improve the diagnostic performance of portable chest (CXR) and abdominal x-rays.  This device (see image below) quantifies angulation of a patient to provide for a better comparison of day-to-day improvement. Potential applications include portable chest and abdominal x-rays performed at patient's hospital bedside.

Development Status:

Available for licensing and commercial development are patent rights covering PET imaging agents, methods of their synthesis, and their uses in imaging specific fungal infections.