University of Central Florida Research Foundation Inc

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Sarfaraz Hussein

Ulas Bagci

Terrence Chen

Vivek Kumar Singh

Matthias John

Naji Khosravan

A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.

Method for detection and diagnosis of lung and pancreatic cancers from imaging scans

Robust calcification tracking is provided in fluoroscopic imagery. A patient with an inserted catheter is scanned over time. A processor detects the catheter in the patient from the scanned image data. The processor tracks the movement of the catheter. The processor also detects a structure represented in the data. The structure is detected as a function of movement with a catheter. The processor tracks the movement of the structure using sampling based on a previous location of the structure in the patient. The processor may output an image of the structure.

Robust calcification tracking in fluoroscopic imaging

A system and method for automatically detecting and quantifying adiposity distribution is presented herein. The system detects, segments and quantifies white and brown fat adipose tissues at the whole-body, body region, and organ levels.

System and method for image-based quantification of white and brown adipose tissue at the whole-body, organ and body-region levels

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Orlando, FL, United States of America

Sarfaraz Hussein