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; wherein obtaining the deep learning model comprises: generating a multi-channel output fully convolutional neural network representative of the triad of predictors; formulating a joint cost function based on a plurality of foreground model weights, a plurality of background model weights and a plurality of shape model weights; minimizing the joint cost function to generate the foreground model comprising the plurality of foreground model weights, the background model comprising the plurality of background model weights, and the shape model comprising the plurality of shape model weights; modifying the joint cost function based on a geometric prior comprising a length shortening prior, a curvature smoothness prior, a shape dictionary prior, and reflectance, processing the input image by the shape model to generate a segmented shape image; processing the input image by the foreground model to generate a foreground texture image; processing the input image by the background model to generate a background texture image; presenting the segmented shape image via a display unit; and presenting the foreground texture image, the background texture image, or both the foreground texture image and the background texture image on the display unit. 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; wherein obtaining the deep learning model comprises: generating a multi-channel output fully convolutional neural network representative of the triad of predictors; formulating a joint cost function based on a plurality of foreground model weights, a plurality of background model weights and a plurality of shape model weights; minimizing the joint cost function to generate the foreground model comprising the plurality of foreground model weights, the background model comprising the plurality of background model weights, and the shape model comprising the plurality of shape model weights; modifying the joint cost function based on a geometric prior comprising a length shortening prior, a curvature smoothness prior, a shape dictionary prior, and reflectance, generating a multi-channel output fully convolutional neural network representative of the triad of predictors; formulating a joint cost function based on a plurality of foreground model weights, a plurality of background model weights and a plurality of shape model weights; minimizing the joint cost function to generate the foreground model comprising the plurality of foreground model weights, the background model comprising the plurality of background model weights, and the shape model comprising the plurality of shape model weights; modifying the joint cost function based on a geometric prior comprising a length shortening prior, a curvature smoothness prior, a shape dictionary prior, and reflectance, processing the input image by the shape model to generate a segmented shape image; processing the input image by the foreground model to generate a foreground texture image; processing the input image by the background model to generate a background texture image; presenting the segmented shape image via a display unit; and presenting the foreground texture image, the background texture image, or both the foreground texture image and the background texture image on the display unit.