Computer-implemented method for executing a deep learning semantic segmentation of RGB digital gonioscopic images representing the irido-corneal interface layers, the method providing a training step comprising the following phases: - receiving a training set comprising a plurality of said RGB digital gonioscopic images and executing a data pre-processing step on said RGB digital gonioscopic images, said data pre-processing step comprising at least a data range scaling to the [0, 1] values range of each channel R-G-B of each pixel of said RGB digital gonioscopic images; - receiving, for each of said RGB digital gonioscopic images of said training set, a number of data maps equal to the number of said layers, each data map representing the pixels of each RGB digital gonioscopic image labeled as appertaining to one of said layers, the pixels of said RGB digital gonioscopic image not considered appertaining to any of said layers being defined as unlabeled pixels appertaining to an un-annotated region; - generating an additional data map representing an estimation of local image informativeness by processing the layer data maps; - providing each scaled RGB digital gonioscopic image of the training set and the associated data maps as the input of a deep neural network comprising an encoder and two decoders, comprising a plurality of levels, said DNN being configured to provide both pixel-wise semantic activation maps to perform semantic segmentation of said layers, through a first semantic decoder, and a pixel-wise activation map to locate a region of interest in said training images by evaluating local image informativeness, preferably lightness and sharpness, through a second ROI decoder, said second ROI decoder operating on a hard copy of data representation output by said encoder; - processing each of said scaled RGB digital gonioscopic images through said DNN and training said DNN through a simultaneous and independent two-loss minimization process: - a first weighted loss for optimizing the weights of the sub-network made up by the sequence of encoder and semantic decoder by assigning each of the pixels in said RGB digital gonioscopic images either a weight different from 0, if appertaining to one of said layers or a weight equal to 0, if appertaining to the un-annotated region, making said sub-network insensitive to un-annotated region during said training step for each RGB digital gonioscopic images of the training set; - a second loss to optimize the weights only of the ROI decoder. - receiving a training set comprising a plurality of said RGB digital gonioscopic images and executing a data pre-processing step on said RGB digital gonioscopic images, said data pre-processing step comprising at least a data range scaling to the [0, 1] values range of each channel R-G-B of each pixel of said RGB digital gonioscopic images; - receiving, for each of said RGB digital gonioscopic images of said training set, a number of data maps equal to the number of said layers, each data map representing the pixels of each RGB digital gonioscopic image labeled as appertaining to one of said layers, the pixels of said RGB digital gonioscopic image not considered appertaining to any of said layers being defined as unlabeled pixels appertaining to an un-annotated region; - generating an additional data map representing an estimation of local image informativeness by processing the layer data maps; - providing each scaled RGB digital gonioscopic image of the training set and the associated data maps as the input of a deep neural network comprising an encoder and two decoders, comprising a plurality of levels, said DNN being configured to provide both pixel-wise semantic activation maps to perform semantic segmentation of said layers, through a first semantic decoder, and a pixel-wise activation map to locate a region of interest in said training images by evaluating local image informativeness, preferably lightness and sharpness, through a second ROI decoder, said second ROI decoder operating on a hard copy of data representation output by said encoder; - processing each of said scaled RGB digital gonioscopic images through said DNN and training said DNN through a simultaneous and independent two-loss minimization process: - a first weighted loss for optimizing the weights of the sub-network made up by the sequence of encoder and semantic decoder by assigning each of the pixels in said RGB digital gonioscopic images either a weight different from 0, if appertaining to one of said layers or a weight equal to 0, if appertaining to the un-annotated region, making said sub-network insensitive to un-annotated region during said training step for each RGB digital gonioscopic images of the training set; - a second loss to optimize the weights only of the ROI decoder. - a first weighted loss for optimizing the weights of the sub-network made up by the sequence of encoder and semantic decoder by assigning each of the pixels in said RGB digital gonioscopic images either a weight different from 0, if appertaining to one of said layers or a weight equal to 0, if appertaining to the un-annotated region, making said sub-network insensitive to un-annotated region during said training step for each RGB digital gonioscopic images of the training set; - a second loss to optimize the weights only of the ROI decoder.