1. A computer image segmentation system for digital images, comprising: a communication interface circuitry; a database; a predictive model repository; and a processing circuitry in communication with the database and the predictive model repository, the processing circuitry configured to: receive a set of training images labeled with a corresponding set of ground truth segmentation masks from the database; establish a fully convolutional neural network comprising a multi-layer contraction convolutional neural network and an expansion convolutional neural network connected in tandem; iteratively train the fully convolution neural network in an end-to-end manner using the set of training images and the corresponding set of ground truth segmentation masks by configuring the processing circuitry to: forward-propagate a training image from the set of training images through the fully convolutional neural network to generate a predictive segmentation mask for the training image; generate an end loss based on a difference between the predictive segmentation mask and a ground truth segmentation mask corresponding to the training image; generate an intermediate loss at one of multiple intermediate layers of the multi-layer contraction convolutional neural network based on an intermediate ground truth segmentation mask downsized from a ground truth segmentation mask corresponding to the training image; back-propagate the end loss and the intermediate loss through the fully convolutional neural network; and minimize a sum of the end loss and the intermediate loss by adjusting a set of training parameters of the fully convolutional neural network using gradient descent; store the iteratively trained fully convolutional neural network with the set of training parameters in the predictive model repository; receive an unlabeled input image; and forward-propagate the unlabeled input image through the iteratively trained convolutional neural network with the set of training parameters to generate an output segmentation mask.