Automatic segmentation method of a given 3D medical image, said method making use of a knowledge database containing information about the anatomical and pathological structures or instruments that can be seen in such a 3D medical image of a × b × n dimension, i.e. a 3D medical image composed of n different 2D images of a x b dimension each, method characterised in that it comprises three process steps, namely: a first step consisting in extracting from said given 3D medical image nine 3D sub-images (1 to 9) of a/2 x b/2 x n dimensions, corresponding to nine partially overlapping a/2 x b/2 2D sub-images from each of the n different 2D images; a second step consisting in nine convolutional neural networks (CNNs) analysing and segmenting each one of these nine 2D sub-images (1 to 9) of each of said n different 2D images; a third step consisting in combining the results of the nine analyses and segmentations of the n different 2D images, and therefore of the nine segmented 3D sub-images with a/2 x b/2 x n dimensions, into a single 3D image with a × b × n dimension, corresponding to a single segmentation of the initial given 3D medical image; wherein the said nine 2D sub-images (1 to 9) of a/2 x b/2 dimension each, are each time extracted as follows from a given 2D image of a x b dimension: - symmetrical partition of the 2D image into four 2D sub-images (1 to 4) by the mediators of the two pairs of opposed sides; - forming two 2D sub-images (5, 6 and 7, 8) having one side in common between them and centered towards the perpendicular sides of the 2D image, according to each of the two directions of said image; - forming a 2D sub-image which is centered towards the 2D image and has its sides parallel to the sides of said image. a first step consisting in extracting from said given 3D medical image nine 3D sub-images (1 to 9) of a/2 x b/2 x n dimensions, corresponding to nine partially overlapping a/2 x b/2 2D sub-images from each of the n different 2D images; a second step consisting in nine convolutional neural networks (CNNs) analysing and segmenting each one of these nine 2D sub-images (1 to 9) of each of said n different 2D images; a third step consisting in combining the results of the nine analyses and segmentations of the n different 2D images, and therefore of the nine segmented 3D sub-images with a/2 x b/2 x n dimensions, into a single 3D image with a × b × n dimension, corresponding to a single segmentation of the initial given 3D medical image; wherein the said nine 2D sub-images (1 to 9) of a/2 x b/2 dimension each, are each time extracted as follows from a given 2D image of a x b dimension: - symmetrical partition of the 2D image into four 2D sub-images (1 to 4) by the mediators of the two pairs of opposed sides; - forming two 2D sub-images (5, 6 and 7, 8) having one side in common between them and centered towards the perpendicular sides of the 2D image, according to each of the two directions of said image; - forming a 2D sub-image which is centered towards the 2D image and has its sides parallel to the sides of said image. - symmetrical partition of the 2D image into four 2D sub-images (1 to 4) by the mediators of the two pairs of opposed sides; - forming two 2D sub-images (5, 6 and 7, 8) having one side in common between them and centered towards the perpendicular sides of the 2D image, according to each of the two directions of said image; - forming a 2D sub-image which is centered towards the 2D image and has its sides parallel to the sides of said image. <EOS>