System and method for image segmentation using a joint deep learning model

A system, comprising:

• an image acquisition unit configured to acquire an input image;
• a display unit;
• a deep learning unit comprising a deep learning model, wherein weights of the deep learning model are updated during training of the deep learning model according to a custom loss function determined based on segmented images output by the deep learning model, autoencoder output images generated by inputting the segmented images to a shape regularization network, and ground truth images, wherein the deep learning unit is configured to process the input image with the deep learning model to generate a segmented image;
• and
• a processor unit communicatively coupled to the deep learning unit and the display unit, wherein the processor unit is configured to present the segmented image via the display unit.

A method, comprising:

• receiving an input image;
• obtaining a deep learning model comprising a triad of predictors including a shape model, a foreground model, and a background model, the deep learning model comprising a multi-channel fully convolutional neural network with the triad of predictors jointly trained according to a joint cost function;
• processing the input image with the shape model of the triad of predictors to generate a segmented image;
• processing the input image with the foreground model of the triad of predictors to generate a foreground texture image;
• processing the input image with the background model of the triad of predictors to generate a background texture image;
• presenting the segmented image via a display unit;
• and
• presenting one or more of the foreground texture image and the background texture image via the display unit.