-- 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 prerequisite. 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.

-- Accurate volume segmentation from the Computed T omography (CT) scan is a common prerequisite for preoperative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). The use of 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task but is memory intensive. The use of patch division can mitigate this issue in practice, but can cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new patch division approach - Patch-512 to tackle the class-imbalance issue by preserving a full field-of-view of the objects in the XY planes. T o achieve better segmentation results based on these asymmetric patches, a 3D DCNN architecture using asymmetrical separable convolutions is proposed. The proposed network, called Z-Net, can be seamlessly integrated into existing 3D DCNNs such as 3D U-Net and V-Net, for improved volume segmentation. Detailed validation of the method is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of the method for intra-operative 3D navigation in robot-assisted MIS. Medical volume segmentation, which labels the class of each voxel in a 3D volume, is a fundamental task in medical image analysis.