公开(公告)号：US20230079164A1

公开(公告)日：2023-03-16

申请号：US17475534

申请日：2021-09-15

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

Inventor： Shanhui Sun ,  Zhang Chen ,  Xiao Chen ,  Terrence Chen ,  Junshen Xu

IPC: G06T7/30 ,  G06N3/02

Abstract: Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.

公开(公告)号：US12141990B2

公开(公告)日：2024-11-12

申请号：US17475534

申请日：2021-09-15

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

Inventor： Shanhui Sun ,  Zhang Chen ,  Xiao Chen ,  Terrence Chen ,  Junshen Xu

IPC: G06T7/30 ,  G06N3/02

Abstract: Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.