Medical image segmentation using an integrated edge guidance module and object segmentation network

A method for performing image segmentation on one or more images, comprising:

• receiving an image comprising one or more objects at a neural network architecture comprising an encoder-decoder network, wherein the encoder-decoder network includes a plurality of encoding blocks and a plurality of decoding blocks;
• generating, using the neural network architecture, an edge-attention representation associated with the image, at least in part, utilizing encoding outputs of one or more encoding blocks included in the encoder-decoder network, wherein the edge-attention representation preserves local edge information included in the image;
• generating, using the neural network architecture, multi-scale feature information associated with the image, at least in part, utilizing decoding outputs of one or more decoding blocks included in the encoder-decoder network, wherein the multi-scale feature information preserves object-level features included in the image;
• generating, using the neural network architecture, segmentation results corresponding to the image using both the edge-attention representation and the multi-scale feature information derived from the image, wherein the segmentation results identify boundaries of the one or more objects in the image.

A computer program product comprising a non-transitory computer-readable medium, including instructions for causing a computer to:

• receive an image comprising one or more objects at a neural network architecture comprising an encoder-decoder network, wherein the encoder-decoder network includes a plurality of encoding blocks and a plurality of decoding blocks;
• generate, using the neural network architecture, an edge-attention representation associated with the image, at least in part, utilizing encoding outputs of one or more encoding blocks included in the encoder-decoder network, wherein the edge-attention representation preserves local edge information included in the image;
• generate, using the neural network architecture, multi-scale feature information associated with the image, at least in part, utilizing decoding outputs of one or more decoding blocks included in the encoder-decoder network, wherein the multi-scale feature information preserves object-level features included in the image;
• generate, using the neural network architecture, segmentation results corresponding to the image using both the edge-attention representation and the multi-scale feature information derived from the image, wherein the segmentation results identify boundaries of the one or more objects in the image.