Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle

TL;DR

This paper introduces Instantiation-Net, a deep learning framework combining DCNN and GCN to reconstruct 3D meshes of the right ventricle from single 2D images, enhancing surgical navigation.

Contribution

It presents a novel end-to-end deep learning approach for 3D mesh reconstruction from 2D images, eliminating manual segmentation and hyper-parameter tuning.

Findings

Effective 3D mesh reconstruction demonstrated

Potential for clinical application shown

Outperforms previous methods in accuracy

Abstract

3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention. It improves the currently less-informative and insufficient 2D navigation schemes for robot-assisted Minimally Invasive Surgery (MIS) to 3D navigation. Previously, a general and registration-free framework was proposed for 3D shape instantiation based on Kernel Partial Least Square Regression (KPLSR), requiring manually segmented anatomical structures as the pre-requisite. Two hyper-parameters including the Gaussian width and component number also need to be carefully adjusted. Deep Convolutional Neural Network (DCNN) based framework has also been proposed to reconstruct a 3D point cloud from a single 2D image, with end-to-end and fully automatic learning. In this paper, an Instantiation-Net is proposed to reconstruct the 3D mesh of a target from its a single 2D image, by using DCNN to extract features from the 2D image and Graph Convolutional Network (GCN) to reconstruct the 3D mesh, and using Fully Connected (FC) layers to connect the DCNN to GCN. Detailed validation was performed to demonstrate the practical strength of the method and its potential clinical use.