Visible Patient

Institut De Recherche Sur Les Cancers De I

Cnam-conservatoire National Des Arts Et Metiers

Institut De Recherche Sur Les Cancers De L'appareil Digestif (ircad)

Nicolas Thome

Jacques Marescaux

Luc Soler

Alexandre Hostettler

This invention concerns an automatic segmentation method of features, such as anatomical and pathological structures or instruments, which are visible in a 3D medical image of a subject, composed of voxels.

Automatic segmentation process of a 3D medical image by several neural networks through structured convolution according to the geometry of the 3D medical image

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&#xd7;b&#xd7;n dimension, i.e. composed of n different 2D images each of a&#xd7;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&#xd7;b/2&#xd7;n dimensions, i.e. nine partially overlapping a/2&#xd7;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&#xd7;b/2&#xd7;n dimensions, into a single image with a&#xd7;b&#xd7;n dimension, corresponding to a single segmentation of the initial medical image.

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

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Champigny sur Marne, France

Nicolas Thome