Abstract: Apparatuses, systems, and techniques to perform registration among images. In at least one embodiment, one or more neural networks are trained to indicate registration of features in common among at least two images by generating a first correspondence by simulating a registration process of registering an image and applying the at least two images and the first correspondence to a neural network to derive a second correspondence of the features in common among the at least two images.

Abstract: Apparatuses, systems, and techniques to generate synthesized images including digital representations of groups of cells blended realistically with appropriate background images. In at least one embodiment, background image data and gene expression data are fused together to generate such a synthesized image using one or more neural networks.

Abstract: Apparatuses, systems, and techniques to perform training of neural networks using stacked transformed images. In at least one embodiment, a neural network is trained on stacked transformed images and trained neural network is provided to be used for processing images from an unseen domain distinct from a source domain, wherein stacked transformed images are transformed according to transformation aspects related to domain variations.

Abstract: Methods include processing image data through a plurality of network stages of a progressively holistically nested convolutional neural network, wherein the processing the image data includes producing a side output from a network stage m, of the network stages, where m>1, based on a progressive combination of an activation output from the network stage m and an activation output from a preceding stage m?1. Image segmentations are produced. Systems include a 3D imaging system operable to obtain 3D imaging data for a patient including a target anatomical body, and a computing system comprising a processor, memory, and software, the computing system operable to process the 3D imaging data through a plurality of progressively holistically nested convolutional neural network stages of a convolutional neural network.

Abstract: A computer-implemented method of post-processing medical image data is provided. The method includes receiving tracked image data representative of multiple blood vessels, generating a binary tree structure for the multiple blood vessels based on a parent-child relationship between branches of the multiple blood vessels, generating a likelihood model for determining a validity of the branches of the multiple blood vessels, and generating a likelihood score for each branch based on the respective branch's compatibility with the likelihood model. The method also includes generating a reconstructed tree for the multiple blood vessels. Compatible branches are included in the reconstructed tree, while invalid branches are not included in the reconstructed tree.

Abstract: A computer-implemented method of post-processing medical image data is provided. The method includes receiving tracked image data representative of multiple blood vessels, generating a binary tree structure for the multiple blood vessels based on a parent-child relationship between branches of the multiple blood vessels, generating a likelihood model for determining a validity of the branches of the multiple blood vessels, and generating a likelihood score for each branch based on the respective branch's compatibility with the likelihood model. The method also includes generating a reconstructed tree for the multiple blood vessels. Compatible branches are included in the reconstructed tree, while invalid branches are not included in the reconstructed tree.