A computer-implemented method for performing segmentation of an input image by using a plurality of images acquired through modification of the input image , the method comprising the steps of: a) receiving the input image and applying a plurality of modification functions to the input image to thereby generate a plurality of modified input images, wherein the modification functions are transition functions configured to alter locations of objects in the modified input images by moving each pixel according to a predetermined algorithm in the input image, such that locations of the same object in the modified input images are different from each other, and wherein the modification functions are configured to keep a scale of each of the plurality of modified input images as in the input image; b) processing each modified input image of the plurality of modified input images by an encoding-decoding convolutional neural network and obtaining in output each of modified feature maps corresponding to said each modified input image, wherein each of the modified feature maps have a segmentation score for each pixel ; c) applying each of reverse transform functions, corresponding to each of the modification functions, to each of the corresponding modified feature maps, wherein the reverse transform functions move each pixel of the modified feature maps to its corresponding location of the input image, to thereby generate each of reverse transform feature maps corresponding to each of the modified feature maps; d) integrating the reverse transform feature maps to thereby obtain an integrated feature map, wherein said integrated feature map is obtained by calculating, for each pixel location of the reverse transformation feature maps, one of a sum, mean, median or maximum value of the segmentation score of each of the reverse transform feature maps at said pixel location; and e) acquiring a result of segmentation by referring to the integrated feature map. a) receiving the input image and applying a plurality of modification functions to the input image to thereby generate a plurality of modified input images, wherein the modification functions are transition functions configured to alter locations of objects in the modified input images by moving each pixel according to a predetermined algorithm in the input image, such that locations of the same object in the modified input images are different from each other, and wherein the modification functions are configured to keep a scale of each of the plurality of modified input images as in the input image; b) processing each modified input image of the plurality of modified input images by an encoding-decoding convolutional neural network and obtaining in output each of modified feature maps corresponding to said each modified input image, wherein each of the modified feature maps have a segmentation score for each pixel ; c) applying each of reverse transform functions, corresponding to each of the modification functions, to each of the corresponding modified feature maps, wherein the reverse transform functions move each pixel of the modified feature maps to its corresponding location of the input image, to thereby generate each of reverse transform feature maps corresponding to each of the modified feature maps; d) integrating the reverse transform feature maps to thereby obtain an integrated feature map, wherein said integrated feature map is obtained by calculating, for each pixel location of the reverse transformation feature maps, one of a sum, mean, median or maximum value of the segmentation score of each of the reverse transform feature maps at said pixel location; and e) acquiring a result of segmentation by referring to the integrated feature map. <EOS>