US 12,357,232 B2
Method for predicting the recurrence of a lesion by image analysis
Estanislao Oubel, Montpellier (FR); Lucien Blondel, Montpellier (FR); Bertin Nahum, Castelnau-le-Lez (FR); and Fernand Badano, Lyons (FR)
Assigned to Quantum Surgical, Montpellier (FR)
Appl. No. 17/925,752
Filed by Quantum Surgical, Montpellier (FR)
PCT Filed May 20, 2021, PCT No. PCT/FR2021/050906
§ 371(c)(1), (2) Date Nov. 16, 2022,
PCT Pub. No. WO2021/234304, PCT Pub. Date Nov. 25, 2021.
Claims priority of application No. 2005338 (FR), filed on May 20, 2020.
Prior Publication US 2023/0172535 A1, Jun. 8, 2023
Int. Cl. G06T 7/11 (2017.01); A61B 5/00 (2006.01); G06T 7/00 (2017.01)
CPC A61B 5/4848 (2013.01) [G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30096 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method for the post-treatment evaluation of an ablation of part of an anatomy of interest of an individual, the anatomy of interest comprising at least one lesion, the ablated part of the anatomy of interest being called an ablation region, the post-treatment evaluation method comprising steps of:
acquiring a post-operative medical image of the anatomy of interest of the individual, comprising all or part of the ablation region;
readjusting the post-operative medical image and a pre-operative medical image of the anatomy of interest of the individual, the readjusted pre-operative medical image and the readjusted post-operative medical image forming a pair of medical images of the anatomy of interest of the individual; and
evaluating a risk of recurrence of the lesion of the anatomy of interest of the individual using a neural network-based machine learning method, analyzing the pair of medical images of the anatomy of interest of the individual, said machine learning method being trained beforehand in a training phase on a database containing a plurality of pairs of medical images of an identical anatomy of interest of a set of patients, each pair of medical images in the database being associated with a recurrence status of a lesion of the anatomy of interest of said patient.