Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.