Automatic segmentation process of a 3d medical image by one or several neural networks through structured convolution according to the anatomic geometry of the 3d medical image

Abstract

This invention concerns an automatic segmentation method of a medical image making use of a knowledge database containing information about the anatomical and pathological structures or instruments, that can be seen in a 3D medical image of a×b×n dimension, i.e. composed of n different 2D images each of a×b dimension. Said method being characterised in that it mainly comprises three process steps, namely: a first step consisting in extracting from said medical image nine sub-images (to) of a/2×b/2×n dimensions, i.e. nine partially overlapping a/2×b/2 sub-images from each 2D image; a second step consisting in nine convolutional neural networks (CNNs) analysing and segmenting each one of these nine sub-images (to) of each 2D image; 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 sub-images with a/2×b/2×n dimensions, into a single image with a×b×n dimension, corresponding to a single segmentation of the initial medical image.