Abstract: Systems and methods for segmenting 3D images are provided. In an embodiment, the system includes a neural network having a low-resolution module trained to infer a complete low-resolution segmentation from an input low-resolution 3D image and to generate corresponding low-resolution feature maps; and a high-resolution module trained to infer a complete high-resolution segmentation from an input high-resolution 3D image and the feature maps from the low-resolution module. Methods for training the neural network and measuring a volume of an object using the 3D segmentations are also described.

Abstract: Systems and methods for detecting and contouring structures of interest in a 3D image are provided. In an embodiment, a method includes detecting structures of interest from a multi-channel input comprising at least one 3D image and generating a corresponding segmentation map using a first neural network; extracting a plurality of cropped images from the at least one 3D image, each cropped image corresponding to a subregion of the at least one 3D image containing at least one of the detected structures; and estimating contours of the detected structures in the plurality of cropped images and generating corresponding shape representations of the estimated contours using a second neural network. A corresponding system and non-transitory computer-readable medium are also provided.

Abstract: Systems and methods for segmenting 3D images are provided. In an embodiment, the system includes a neural network having a low-resolution module trained to infer a complete low-resolution segmentation from an input low-resolution 3D image and to generate corresponding low-resolution feature maps; and a high-resolution module trained to infer a complete high-resolution segmentation from an input high-resolution 3D image and the feature maps from the low-resolution module. Methods for training the neural network and measuring a volume of an object using the 3D segmentations are also described.