Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Abstract

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

Soft Tissue Feature Tracking Based on Deep Matching Network

“…Tracking research on the surface of soft tissue is conducive to the use of surgical robots' high-precision and high-flexibility characteristics. It can perform precise surgical operations in different organs and tissues of the human body [10][11][12]. It is conducive to the recovery and reconstruction of surgical organs and tissues, greatly reduces the danger caused by the shaking of the body during the operation of the surgeon, greatly enhances the doctor's confidence and reduces the surgeon's fatigue, and enhances the safety and effectiveness of the operation.…”

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering

“…The RMSE is the square root of the mean square error (MSE). MSE is the ratio of the sum of squares of the voxel value errors between the reconstructed image and the true value image to the number of voxels ( 54 , 55 ), defined as the formula (2):…”

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching

“…Generally, the transformation model with eight parameters is mainly used in splicing, the matrix transformation with 8 degrees of freedom. Because a pair of registration points can support two equations to establish the relationship, four non-collinear matching pairs are needed to calculate the parameters (Zhang et al, 2021 ).…”