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
US11288812B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jan 10, 2019 |
| Grant date | Mar 29, 2022 |
| Priority date | — |
| Expiry date | Jan 10, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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 (1 to 9) 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 (1 to 9) 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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.