Patent · US Active

3D segmentation of lesions in CT images using self-supervised pretraining with augmentation

US12175679B2 · kind B2 · utility

0Cited by

0References

17Claims

0Family size

Assignee

Merck Sharp & Dohme LLC · US

Inventors

Yiqiao Liu · Lo Wu, CN
Antong Chen · Blue Bell, US
Gregory Goldmacher · Lansdale, US

Key dates

Filing date	Nov 28, 2023
Grant date	Dec 24, 2024
Priority date	—
Expiry date	Nov 28, 2043

Classification

Technology area (CPC G)Physics
CPC primaryG06V2201/03
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D slices above and below the annotated slice. The system then fine-tunes the pre-trained encoder using an annotated 2D image dataset. The annotated 2D image dataset includes multiple 2D slices of lesions, each including an annotation that delineates a boundary of a corresponding lesion.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.