CCCD: Corner detection and curve reconstruction for improved 3D surface reconstruction from 2D medical images

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must be in ordered coordinate pairs. This ordering problem is resolved using curve reconstruction (CR) or chain code (CC) algorithms. Existing CR methods lead to issues like holes, while some chain codes have junction-induced errors that need preprocessing. To address the above issues, a new graph neural network (GNN)-based approach named curvature and chain code-based corner detection (CCCD) is introduced that not only orders the CTPs but also removes junction errors. The goal is to improve accuracy and reliability in generating smooth surfaces. The paper fuses well-known CD methods with a curve generation technique and compares these alternative fused methods with CCCD. CCCD is also compared against other curve reconstruction techniques to establish its superiority. For validation, CCCD's accuracy in predicting boundaries is compared with deep learning models like Polar U-Net, KiU-Net 3D, and HdenseUnet, achieving an impressive Dice score of 98.49%, even with only 39.13% boundary points.