Uncertainty-refined image segmentation under domain shift

A system for digital image segmentation, the system comprising:

• a storage device configured to store program instructions;
• and
• one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• train a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training;
• receive, by the neural network, image data from a second, different domain, wherein the image data comprises a number of image elements;
• calculate, by the neural network, a vector of N values that sum to 1 for each image element, wherein each of the N values represents an image segmentation class;
• assign, by the neural network, a segmentation label to each image element, wherein the segmentation label corresponds to a segmentation class with a highest value in the vector calculated for the image element;
• perform, by the neural network with active dropout layers, multiple inferences for each image element;
• generate, by the neural network, an uncertainty value for each image element according to the inferences;
• and
• replace the segmentation label of any image element with an uncertainty value above a predefined threshold with a new segmentation label corresponding to a segmentation class with a next highest value in the vector for that image element.

A computer program product for digital image segmentation, the computer program product comprising:

• a non-transitory computer-readable storage medium having program instructions embodied thereon to perform the steps of:
• training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training;
• receiving, by the neural network, image data from a second, different domain, wherein the image data comprises a number of image elements;
• calculating, by the neural network, a vector of N values that sum to 1 for each image element, wherein each of the N values represents an image segmentation class;
• assigning, by the neural network, a segmentation label to each image element, wherein the segmentation label corresponds to a segmentation class with a highest value in the vector calculated for the image element;
• performing, by the neural network with active dropout layers, multiple inferences for each image element;
• generating, by the neural network, an uncertainty value for each image element according to the inferences;
• and
• replacing the segmentation label of any image element with an uncertainty value above a predefined threshold with a new segmentation label corresponding to a segmentation class with a next highest value in the vector for that image element.