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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.

Chronic leg ulcers are a debilitating vasculopathic complication for some patients with sickle cell disease (SCD). Prevalence of leg ulcers varies based on age and geographic location; about 5-10% of all SCD patients may suffer leg ulcers. These leg ulcers are painful, result in infections, hospitalization, disability, and negatively impact the patient’s social and psychological wellbeing on an ongoing basis.

Prostate cancer is the most common male cancer in the United States, and the third most common worldwide. Prostate biopsies are often performed to confirm a cancer diagnosis and examine suspect tissue. Prostate biopsies are most often performed under transrectal ultrasound imaging (TRUS) guidance. TRUS images in real-time, at relatively low cost, and shows both prostate and boundaries. However, major problems with TRUS imaging are poor spatial resolution and low sensitivity for cancer detection.

Crouch gait is a common disorder in pediatric cerebral palsy (CP). Effective treatment of crouch during childhood is critical to maintain mobility into adulthood. Current interventions do not alleviate crouch gait long-term for most patients. This technology relates to a powered exoskeleton designed for gait assistance. The powered assistance may provide a physical therapy-type intervention to improve and maintain mobility.  

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.
 

Ultrasound-based cancer screening and biopsy imaging technologies are a clinical need. Ultrasound based biopsy imaging can provide a real-time modality for lower cost that is comparable to, or complimentary to MRI imaging.  This technology may enable more accurate, less costly and more accessible cancer screening.

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.

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.

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.