Lawrence Livermore National Security, LLC

Adobe, Inc.

Arizona State University

Hyojin Kim

Jayaraman Jayaraman Thiagarajan

Peer-Timo Bremer

Rushil Anirudh

Kyle Champley

Viswanathan Swaminathan

Suren Jayasuriya

Kadri Aditya Mohan

Albert William Reed

A system for generating 2D slices of a 3D image of a target volume is provided. The system receives a target sinogram collected during a computed tomography scan of the target volume. The system inputs the target sinogram to a convolutional neural network (CNN) to generate predicted 2D slices of the 3D image. The CNN is trained using training 2D slices of training 3D images. The system initializes 2D slices to the predicted 2D slices. The system reconstructs 2D slices of the 3D image from the target sinogram and the initialized 2D slices.

Few-view computed tomography reconstruction using deep neural network inference

A system for generating a 4D representation of a scene in motion given a sinogram collected from the scene while in motion. The system generates, based on scene parameters, an initial 3D representation of the scene indicating linear attenuation coefficients (LACs) of voxels of the scene. The system generates, based on motion parameters, a 4D motion field indicating motion of the scene. The system generates, based on the initial 3D representation and the 4D motion field, a 4D representation of the scene that is a sequence of 3D representations having LACs. The system generates a synthesized sinogram of the scene from the generated 4D representation. The system adjusts the scene parameters and the motion parameters based on differences between the collected sinogram and the synthesized sinogram. The processing is repeated until the differences satisfy a termination criterion.

Reconstruction of dynamic scenes based on differences between collected view and synthesized view

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Identification of uncommon objects in containers

A system for identifying objects in an image is provided. The system identifies segments of an image that may contain objects. For each segment, the system generates a segment score by inputting to a multi-scale neural network windows of multiple scales that include the segment that have been resampled to a fixed window size. A multi-scale neural network includes a feature extracting convolutional neural network ('feCNN') for each scale and a classifier that inputs each feature of each feCNN. The segment score indicates whether the segment contains an object. The system generates a pixel score for pixels of the image. The pixel score for a pixel indicates that that pixel is within an object based on the segment scores of segments that contain that pixel. The system then identifies the object based on the pixel scores of neighboring pixels.

Multi-scale deep learning system

A pair of video streams is prepared for stereoscopic display. A pair of frames is rectified. Each of the pair of frames is from a respective stream of the pair of video streams. A reduced video stream is generated by removing from one of the pair of video streams a set of correlative data that is present in or can be predicted from the other of the pair of video streams. The reduced video stream and the other of the pair of video streams are compressed for use in stereoscopic display of the reduced video stream and the other of the pair of video streams.

Growing community of inventors

Livermore, CA, United States of America

Hyojin Kim