公开(公告)号：US11749401B2

公开(公告)日：2023-09-05

申请号：US17084875

申请日：2020-10-30

Applicant: Guerbet

Inventor： Yi-Qing Wang ,  Giovanni John Jacques Palma

IPC: G16H30/40 ,  G06T7/00 ,  G06T7/187 ,  G06T5/00 ,  G06T19/20 ,  G06F18/2413

CPC classification number: G16H30/40 ,  G06F18/24147 ,  G06T5/002 ,  G06T7/0012 ,  G06T7/187 ,  G06T19/20 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2219/004 ,  G06V2201/031

Abstract: A mechanism is provided for seed relabeling for seed-based slice-wise lesion segmentation. The mechanism receives a lesion mask for a three-dimensional medical image volume. The lesion mask corresponds to detected lesions in the medical image volume and wherein each detected lesion has a lesion contour. The mechanism generates a distance map for a given two-dimensional slice in the medical image volume based on the lesion mask. The distance map comprises a distance to a lesion contour for each voxel of the given two-dimensional slice. The mechanism performs local maxima identification to select a set of local maxima from the distance map such that each local maximum has a value greater than its immediate neighbor points. The mechanism performs seed relabeling based on the distance map and the set of local maxima to generate a set of seeds. Each seed represents a center of a distinct component of a lesion contour. The mechanism performs image segmentation on the lesion mask based on the set of seeds to form a split lesion mask.

公开(公告)号：US11688065B2

公开(公告)日：2023-06-27

申请号：US17740909

申请日：2022-05-10

Applicant: Guerbet

Inventor： Giovanni John Jacques Palma ,  Pedro Luis Esquinas Fernandez ,  Paul Dufort ,  Thomas Binder ,  Arkadiusz Sitek ,  Dana Levanony ,  Yi-Qing Wang ,  Omid Bonakdar Sakhi

IPC: G06T7/00 ,  G06T7/11

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30056 ,  G06T2207/30096

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.