University of California

Pratik Mukherjee

Esther L Yuh

Geoffrey T Manley

Christian Haene

Jitendra Malik

Wei-cheng Kuo

A computer-implemented method can include a training phase and a hemorrhage detection phase. The training phase can include: receiving a first plurality of frames from at least one original computed tomography (CT) scan of a target subject, wherein each frame may or may not include a visual indication of a hemorrhage, and further wherein each frame including a visual indication of a hemorrhage has at least one label associated therewith; and using a fully convolutional neural network (FCN) to train a model by determining, for each of the first plurality of frames, whether at least one sub-portion of the frame includes a visual indication of a hemorrhage and classifying the sub-portion of the frame based on the determining. The hemorrhage detection phase can include: receiving a second plurality of frames from a CT scan of a target subject, wherein each frame may or may not include a visual indication of a hemorrhage; and determining, for each of the second plurality of frames, whether a plurality of sub-portions of the frame includes a visual indication of a hemorrhage based at least in part on the trained model.

Expert-level detection of acute intracranial hemorrhage on head CT scans

A computer-based method for quantitative evaluation of computed tomography (CT) images of the head, particularly in circumstances of neurological emergency such as acute intracranial hemorrhage, evidence of intracranial mass effect, and acute stroke. The method comprises: calculation of volumes of abnormal areas such as locations of hemorrhage; quantification of severity of midline shift and basilar cistern effacement; and rapid identification of anatomical locations of abnormal findings. The methods comprise use of heuristics, convolutional neural networks, deep learning, edge detection, and Hough transform.

Growing community of inventors

San Francisco, CA, United States of America

Pratik Mukherjee