3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes

US10565707B2 · kind B2 · utility

Assignee

• Siemens Healthcare Limited · DE

Inventors

• Siqi LIU · Dongguan, CN
• Daguang Xu · Princeton, US
• Shaohua Kevin Zhou · Plainsboro, US
• Thomas Mertelmeier · Erlangen, DE
• Julia WICKLEIN · Neunkirchen, DE
• Anna Jerebko · Eschborn, DE
• Sasa Grbic · Plainsboro, US
• Olivier Pauly · Munich, DE
• Dorin Comaniciu · Princeton Junction, US

Key dates

• Filing date: Jun 4, 2018
• Grant date: Feb 18, 2020
• Expiry date: Oct 18, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30068
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A computer-implemented method for identifying features in 3D image volumes includes dividing a 3D volume into a plurality of 2D slices and applying a pre-trained 2D multi-channel global convolutional network (MC-GCN) to the plurality of 2D slices until convergence. Following convergence of the 2D MC-GCN, a plurality of parameters are extracted from a first feature encoder network in the 2D MC-GCN. The plurality of parameters are transferred to a second feature encoder network in a 3D Anisotropic Hybrid Network (AH-Net). The 3D AH-Net is applied to the 3D volume to yield a probability map;. Then, using the probability map, one or more of (a) coordinates of the objects with non-maximum suppression or (b) a label map of objects of interest in the 3D volume are generated.