A learning method for a segmentation of a training image using a learning device, wherein the learning device includes (i) a first to a k-th convolutional layers configured to respectively generate a first to a k-th encoded feature maps by applying one or more convolution operations to at least one feature map corresponding to the training image, (ii) a k-th to a first deconvolutional layers configured to respectively generate a k-th to a first decoded feature maps by applying one or more deconvolution operations to the k-th encoded feature map, (iii) a first to an h-th mask layers configured to respectively correspond to h convolutional layers among the k convolutional layers, and (iv) a first to an h-th differential layers configured to respectively correspond to the first to the h-th mask layers, and wherein the h is an integer from 1 to (k-1), the learning method comprising steps of: (a) if the training image is acquired, instructing, by the learning device, the first to the k-th convolutional layers to generate the first to the k-th encoded feature maps through processes of instructing the first to the h-th mask layers to extract one or more edge parts from h encoded feature maps, to be respectively inputted to the first to the h-th mask layers, among the first to the k-th encoded feature maps by acquiring one or more areas in which frequencies of the h encoded feature maps are larger than their corresponding threshold, to thereby generate a first to an h-th bandpass feature maps, and instructing the first to the h-th differential layers to generate a first to an h-th differential feature maps by calculating each difference between each of the h encoded feature maps, to be respectively inputted to the first to the h-th mask layers, and each of the first to the h-th bandpass feature maps, to thereby forward each of the first to the h-th differential feature maps to their corresponding next convolutional layers; (b) instructing, by the learning device, the k-th to the first deconvolutional layers to sequentially generate the k-th to the first decoded feature maps (i) by summing the h-th to the first bandpass feature maps and corresponding h decoded feature maps each of which is outputted from each previous deconvolutional layer of each of h deconvolutional layers corresponding to the h-th to the first mask layers and (ii) by using feature maps outputted from each previous layer of each of k-h deconvolutional layers, which do not correspond to the h-th to the first mask layers; and (c) backpropagating, by the learning device, one or more losses generated by comparing the first decoded feature map which is used to generate a segmented label image of the training image and its corresponding at least one GT (ground truth) label image, to thereby adjust one or more parameters of at least part of the first to the k-th deconvolutional layers and the k-th to the first convolutional layers. (a) if the training image is acquired, instructing, by the learning device, the first to the k-th convolutional layers to generate the first to the k-th encoded feature maps through processes of instructing the first to the h-th mask layers to extract one or more edge parts from h encoded feature maps, to be respectively inputted to the first to the h-th mask layers, among the first to the k-th encoded feature maps by acquiring one or more areas in which frequencies of the h encoded feature maps are larger than their corresponding threshold, to thereby generate a first to an h-th bandpass feature maps, and instructing the first to the h-th differential layers to generate a first to an h-th differential feature maps by calculating each difference between each of the h encoded feature maps, to be respectively inputted to the first to the h-th mask layers, and each of the first to the h-th bandpass feature maps, to thereby forward each of the first to the h-th differential feature maps to their corresponding next convolutional layers; (b) instructing, by the learning device, the k-th to the first deconvolutional layers to sequentially generate the k-th to the first decoded feature maps (i) by summing the h-th to the first bandpass feature maps and corresponding h decoded feature maps each of which is outputted from each previous deconvolutional layer of each of h deconvolutional layers corresponding to the h-th to the first mask layers and (ii) by using feature maps outputted from each previous layer of each of k-h deconvolutional layers, which do not correspond to the h-th to the first mask layers; and (c) backpropagating, by the learning device, one or more losses generated by comparing the first decoded feature map which is used to generate a segmented label image of the training image and its corresponding at least one GT (ground truth) label image, to thereby adjust one or more parameters of at least part of the first to the k-th deconvolutional layers and the k-th to the first convolutional layers.