A learning method for improving image segmentation by using a learning device, wherein the learning device includes (i) an encoding layer having each of a -th to a (1-K)-th filters respectively generating a -th to a (1-K)-th feature maps by applying one or more convolution operations to a training image as an input image; (ii) a decoding layer having each of a (2-K)-th to a -th filters respectively generating a (2-K)-th to a -th feature maps by applying one or more deconvolution operations, wherein the (2-K) -th feature map is generated by applying one or more deconvolution operations to the (1-K)-th feature map; and (iii) an intermediate layer having each of a -th to a (3-H)-th filters respectively arranged between each of H encoding filters among the K filters included in the encoding layer and each of H decoding filters among the K filters included in the decoding layer, comprising steps of: (a) the learning device, if the input image is obtained, acquiring the -th to the (1-K)-th feature maps through the encoding layer; (b) the learning device acquiring a -th to a (3-H)-th feature maps by respectively inputting each output of the H encoding filters to the -th to the (3-H)-th filters; (c) the learning device performing a process of sequentially acquiring the (2-K)-th to the -th feature maps by (i) each of the respective H decoding filters respectively using both a corresponding feature map from the -th to the (3-H)-th feature maps and a corresponding feature map from the (2-K)-th to the -th feature maps obtained from the respective previous decoding filter of the respective H decoding filters and by (ii) each of the respective K-H decoding filters that are not associated with the -th to the (3-H)-th filters using a corresponding feature map from the (2-K)-th to the -th feature maps obtained from the respective previous decoding filter of the respective K-H decoding filters; and (d) the learning device adjusting parameters of at least part of the -th to the (2-K)-th filters and the -th to the (3-H)-th filters and the -th to the (1-K)-th filters by performing a backpropagation process with a difference between a Ground Truth label image and the -th feature map, wherein the learning device, at the step of (a), generates the -th to the (1-K)-th feature maps by the -th to the (1-K)-th filters respectively reducing sizes of their corresponding feature maps while increasing number of channels thereof; wherein the learning device, at the step of (b), generates the -th to the (3-H)-th feature maps by the -th to the (3-H)-th filters extracting edges information from the output of the H encoding filters; and wherein the learning device, at the step of (c), generates the (2-K)-th to the -th feature maps by the (2-K)-th to the -th filters respectively increasing sizes of inputs fed into the (2-K)-th to the -th filters while decreasing the number of channels thereof. (a) the learning device, if the input image is obtained, acquiring the -th to the (1-K)-th feature maps through the encoding layer; (b) the learning device acquiring a -th to a (3-H)-th feature maps by respectively inputting each output of the H encoding filters to the -th to the (3-H)-th filters; (c) the learning device performing a process of sequentially acquiring the (2-K)-th to the -th feature maps by (i) each of the respective H decoding filters respectively using both a corresponding feature map from the -th to the (3-H)-th feature maps and a corresponding feature map from the (2-K)-th to the -th feature maps obtained from the respective previous decoding filter of the respective H decoding filters and by (ii) each of the respective K-H decoding filters that are not associated with the -th to the (3-H)-th filters using a corresponding feature map from the (2-K)-th to the -th feature maps obtained from the respective previous decoding filter of the respective K-H decoding filters; and (d) the learning device adjusting parameters of at least part of the -th to the (2-K)-th filters and the -th to the (3-H)-th filters and the -th to the (1-K)-th filters by performing a backpropagation process with a difference between a Ground Truth label image and the -th feature map,