Point Cloud Registration in Laparoscopic Liver Surgery Using Keypoint Correspondence Registration Network.

Laparoscopic liver surgery is a newly developed minimally invasive technique and represents an inevitable trend in the future development of surgical methods. By using augmented reality (AR) technology to overlay preoperative CT models with intraoperative laparoscopic videos, surgeons can accurately locate blood vessels and tumors, significantly enhancing the safety and precision of surgeries. Point cloud registration technology is key to achieving this effect.

IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation.

Accurate segmentation of medical images plays an essential role in their analysis and has a wide range of research and application values in fields of practice such as medical research, disease diagnosis, disease analysis, and auxiliary surgery. In recent years, deep convolutional neural networks have been developed that show strong performance in medical image segmentation. However, because of the inherent challenges of medical images, such as irregularities of the dataset and the existence of outliers, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical employment.

MHSU-Net: A more versatile neural network for medical image segmentation.

Background And Objective: Medical image segmentation plays an important role in clinic. Recently, with the development of deep learning, many convolutional neural network (CNN)-based medical image segmentation algorithms have been proposed. Among them, U-Net is one of the most famous networks.

Hybrid dilation and attention residual U-Net for medical image segmentation.

Medical image segmentation is a typical task in medical image processing and critical foundation in medical image analysis. U-Net is well-liked in medical image segmentation, but it doesn't fully explore useful features of the channel and capitalize on the contextual information. Therefore, we present an improved U-Net with residual connections, adding a plug-and-play, very portable channel attention (CA) block and a hybrid dilated attention convolutional (HDAC) layer to perform medical image segmentation for different tasks accurately and effectively, and call it HDA-ResUNet, in which we fully utilize advantages of U-Net, attention mechanism and dilated convolution.

A Novel Haptic Interactive Approach to Simulation of Surgery Cutting Based on Mesh and Meshless Models.

In the present work, the majority of implemented virtual surgery simulation systems have been based on either a mesh or meshless strategy with regard to soft tissue modelling. To take full advantage of the mesh and meshless models, a novel coupled soft tissue cutting model is proposed. Specifically, the reconstructed virtual soft tissue consists of two essential components.

A high-resolution model for soft tissue deformation based on point primitives.

Background And Objectives: In order to achieve a high degree of visual realism in surgery simulation, we propose a new model, which is based on point primitives and continuous elastic mechanics theory, for soft tissue deformation, tearing and/or cutting.

Methods: The model can be described as a two-step local high-resolution strategy. First, appropriate volumetric data are sampled and assigned with proper physical properties.

A new deformation simulation algorithm for elastic-plastic objects based on splat primitives.

To achieve high computational efficiency and realistic visual effects, a new simulation algorithm for soft tissue deformation, which is based on a shape-matching scheme using splat primitives, is presented for interactive real-time applications, such as surgery simulation and video games. The most important novelty of the proposed approach lies in the fact that surface splats instead of points are employed in the computation of the deformation and fracturing of an elastic-plastic object. By controlling the sampling density and automatically adjusting the size of the circular splats, the surface of the simulated object can be seamlessly covered with a much small number of splats than points.

A New Deformation Model of Biological Tissue for Surgery Simulation.

A novel meshless deformation model of biological soft tissue, which is mainly based on the radial basis function point interpolation, is presented for interactive simulation applications such as virtual surgery simulators. Compared with conventional mesh models, the proposed model is particularly suitable for simulating large deformation, sucking and cutting tasks since there is no need to maintain grid information. Kelvin viscoelasticity, which represents relaxation, creep, and hysteresis of soft tissue, is integrated into the proposed model, making the simulation much more realistic than many existing meshless models.