A full-set tooth segmentation model based on improved PointNet++

Abstract

Segmentation of a complete set of teeth from three-dimensional (3D) intra-oral scanner images is a crucial step in tooth identification procedures. In large-scale disasters with many victims, teeth are often the preferred and reliable source for victim identification due to their hard and non-deformable characteristics. In this paper we present a study on the automatic segmentation of a complete set of teeth from intra-oral scanner images. We propose a tooth segmentation method based on an improved PointNet++ architecture. To address the problem of inadequate segmentation capability of the teeth-gingival boundary of PointNet++, we introduce a single-point preliminary feature extraction (SPFE) module to better preserve the subtle details that may be overlooked by the original PointNet++ model. In addition, a weighted-sum local feature aggregation (WSLFA) mechanism is proposed to replace the max pooling in PointNet++ to better perform feature aggregation. The experimental results on 52 testing datasets using the network trained on 160 annotated 3D intra-oral scanner images demonstrate that our improved PointNet++ method achieves a segmentation accuracy of 97.68%, and performs well under different dental conditions.