Framework for integrating deformable modeling with 3d deep neural network segmentation

Abstract

Methods for image segmentation and smoothing of image segmentations are provided. In various embodiments, a plurality of training images is provided to a segmentation network. A candidate segmentation is obtained from the segmentation network for each of the plurality of training images. Each candidate segmentation is compared to a ground truth segmentation to compute a loss metric for each candidate segmentation. Based on the gradient of the loss, the segmentation network is trained to minimize level set smoothing energy. In various embodiments, an input image is downsampled from a first resolution to a second, lower resolution. The downsampled image is provided to a segmentation network. A segmentation at the second resolution is obtained from the segmentation network. The segmentation from the second resolution is upsampled to the first resolution. The input image and the upsampled segmentation are provided at the first resolution to a convolutional network. An updated segmentation at the first resolution is obtained therefrom.