SCS-Voxel2Mesh: a Self-Calibrated Separable 3D Mesh Generation Network for Lung Nodule Spikes Classification and Malignancy Prediction

Radiologists use the standardized Lung-RADS clinical scoring criteria to assess and report spiculations/lobulations and sharp/curved spikes on the surface of lung nodules, because they are good predictors of lung cancer. Manual spiculation/lobulation annotation and classification is a tedious task for radiologists due to the nodule’s 3D geometry and 2D slice-by-slice assessment. This work presents SCS-Voxel2Mesh, a multi-class end-to-end deep learning model that segments pulmonary nodules (while preserving spikes), classifies spikes (sharp/spiculation and curved/lobulation) and performs malignancy prediction. Comprehensive experiments were conducted to evaluate and compare the performance of SCS-Voxel2Mesh with other segmentation and 3D mesh generation methods using the CIR dataset. The results demonstrate competitive performance of SCS-Voxel2Mesh on both the nodule spike classification and malignancy prediction tasks.