Modelling a dense network connectivity for panoptic tooth segmentation using learning approaches

Abstract

By enabling precise diagnoses and assisting in creating successful treatment regimens, teeth segmentation plays a crucial role in dentistry. The segmentation of teeth has been the primary emphasis of previous approaches, but they frequently ignore the context of the oral tissue as a whole. According to this study, background semantic and foreground instance-based segmentation results are combined in a panoptic-segmentation-based approach. This work provides a tailored architecture for segmenting using a Super Adaptive Dense Convolutional Neural Network (sa−DCNN) with a panoptic quality (PQ) loss function. The PQ loss function speeds up learning and enables the model to predict masks and the classes that go with them instantly. In our suggested design, sa−DCNN enables connectivity between the memory path and the sa−DCNN pixel route. A stacked network block integrating multi-scale properties from several decoding resolutions is also included. The network block's primary duties involve integrating various mechanisms. The output heads analyze the memory path and pixel route data to predict the mask classes used to create the final mask. The proposed model outperforms the most recent panoptic segmentation on the UFBA-UESC Dental Image dataset, notably in performance and resilience. Our study represents a significant advancement in teeth segmentation and advances our knowledge of oral anatomy.

Keywords

• Tooth segmentation
• Adaptive network
• Prediction
• Panoptic segmentation
• Quality