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-1)-th to a first deconvolutional layers configured to respectively generate a (k-1)-th to a first decoded feature maps by applying one or more deconvolution operations to the k-th encoded feature map, wherein a segmentation label image of the training image is generated from the first decoded feature map, (iii) a first to an h-th mask layers configured to respectively correspond to h deconvolutional layers among the (k-1) deconvolutional layers, and (iv) a first to an h-th edge loss 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), comprising steps of: (a) if the training image is inputted, instructing, by the learning device, the first to the k-th convolutional layers to generate the first to the k-th encoded feature maps; (b) instructing, by the learning device, the (k-1)-th to the first deconvolutional layers to sequentially generate the (k-1)-th to the first decoded feature maps, wherein the learning device instructs the h-th to the first mask layers to (i) generate an h-th to a first edge feature maps by extracting edge parts from h original decoded feature maps each of which is outputted from each of the h deconvolutional layers corresponding to each of the h-th to the first mask layers, and (ii) generate h edge reinforced decoded feature maps serving as at least part of the (k-1)-th to the first decoded feature maps by adding the h original decoded feature maps and the h-th to the first edge feature maps, wherein the (k-1)-th to the first deconvolutional layers sequentially generate the (k-1)-th to the first decoded feature maps (i) by generating the h edge reinforced decoded feature maps serving as the at least part of the (k-1)-th to the first decoded feature maps by element-wise adding each of the h-th to the first edge feature maps and each of the h original decoded feature maps outputted from the h deconvolutional layers corresponding to the h-th to the first mask layers and (ii) by generating k-h-1 original decoded feature maps serving as part of the k-1 decoded feature maps via applying the deconvolution operations to feature maps from each previous layer of each of k-h-1 deconvolutional layers, which do not correspond to the h-th to the first mask layers; and (c) instructing, by the learning device, the first to the h-th edge loss layers to generate a first to an h-th edge losses by calculating differences between the edge parts and their corresponding ground truths, GTs, wherein the GTs are respectively extracted from a first to an h-th GT images whose sizes correspond respectively to sizes of the first to the h-th edge feature maps, to thereby adjust one or more parameters of the first to the (k-1)-th deconvolutional layers and the k-th to the first convolutional layers through backpropagation by using the first to the h-th edge losses. (a) if the training image is inputted, instructing, by the learning device, the first to the k-th convolutional layers to generate the first to the k-th encoded feature maps; (b) instructing, by the learning device, the (k-1)-th to the first deconvolutional layers to sequentially generate the (k-1)-th to the first decoded feature maps, wherein the learning device instructs the h-th to the first mask layers to (i) generate an h-th to a first edge feature maps by extracting edge parts from h original decoded feature maps each of which is outputted from each of the h deconvolutional layers corresponding to each of the h-th to the first mask layers, and (ii) generate h edge reinforced decoded feature maps serving as at least part of the (k-1)-th to the first decoded feature maps by adding the h original decoded feature maps and the h-th to the first edge feature maps, wherein the (k-1)-th to the first deconvolutional layers sequentially generate the (k-1)-th to the first decoded feature maps (i) by generating the h edge reinforced decoded feature maps serving as the at least part of the (k-1)-th to the first decoded feature maps by element-wise adding each of the h-th to the first edge feature maps and each of the h original decoded feature maps outputted from the h deconvolutional layers corresponding to the h-th to the first mask layers and (ii) by generating k-h-1 original decoded feature maps serving as part of the k-1 decoded feature maps via applying the deconvolution operations to feature maps from each previous layer of each of k-h-1 deconvolutional layers, which do not correspond to the h-th to the first mask layers; and (c) instructing, by the learning device, the first to the h-th edge loss layers to generate a first to an h-th edge losses by calculating differences between the edge parts and their corresponding ground truths, GTs, wherein the GTs are respectively extracted from a first to an h-th GT images whose sizes correspond respectively to sizes of the first to the h-th edge feature maps, to thereby adjust one or more parameters of the first to the (k-1)-th deconvolutional layers and the k-th to the first convolutional layers through backpropagation by using the first to the h-th edge losses.