Apparatus and method for image segmentation using a deep convolutional neural network with a nested U-structure

A method, comprising:

• at a computer system that includes a processor and memory:
• receiving an image;
• applying the image to a sequence of encoders that includes a first encoder and one or more subsequent encoders, wherein each encoder in the sequence of encoders comprises a U network encoder which performs convolution neural network processing in an orthogonal dimension, and the one or more subsequent encoders receive a down-sampled version of the image from a prior encoder, the sequence of encoders forming a first dimension orthogonal to the orthogonal dimension;
• applying a result of the sequence of encoders to a sequence of decoders that includes a first decoder and one or more subsequent decoders, wherein a respective decoder in the one or more subsequent decoders comprises a U network decoder that performs convolution neural network processing of an up-sampled version of the image from the first decoder, and the sequence of decoders forms a second dimension parallel to the first dimension;
• producing probability outputs from paired encoders and decoders in the sequence of encoders and the sequence of decoders;
• and
• combining the probability outputs to form a final output.

A computer system, comprising:

• a processor;
• and
• memory storing one or more programs, the one or more programs including instructions for:
• receiving an image;
• applying the image to a sequence of encoders that includes a first encoder and one or more subsequent encoders, wherein each encoder in the sequence of encoders comprises a U network encoder which performs convolution neural network processing in an orthogonal dimension, and the one or more subsequent encoders receive a down-sampled version of the image from a prior encoder, the sequence of encoders forming a first dimension orthogonal to the orthogonal dimension;
• applying a result of the sequence of encoders to a sequence of decoders that includes a first decoder and one or more subsequent decoders, wherein a respective decoder in the one or more subsequent decoders comprises a U network decoder that performs convolution neural network processing of an up-sampled version of the image from the first decoder, and the sequence of decoders forms a second dimension parallel to the first dimension;
• producing probability outputs from paired encoders and decoders in the sequence of encoders and the sequence of decoders;
• and
• combining the probability outputs to form a final output.