LeFUNet: UNet with Learnable Feature Connections for Teeth Identification and Segmentation in Dental Panoramic X-ray Images

Deep learning methods have widely been applied to accurately identify and segment individual teeth in panoramic X-ray radiographs. However, the task becomes challenging as deep learning models grow deeper and wider. Contextual information have to pass through many layers leading to features vanishing before reaching the end of the model. This study proposes a deep learning-based three-step model to identify and segment individual teeth from panoramic X-ray radiographs to address the issues above. Firstly, an automatic binarized transformation of panoramic images to deal with computational complexity of high-dimensionality with respect to less training data is conducted. The transformed images are then trained on with a vanilla top-down learnable feature connection based UNet. Specifically, a learnable feature connection module that incorporates an improved squeeze and excitation module with dense connections to ensure feature propagation and facilitating information flow throughout the network is designed and conveniently plunged into a UNet. Finally, accurate individual tooth identification and segmentation is achieved using proposed regions via bounding detection techniques. Extensive evaluation on a publicly available dental panoramic X-ray benchmark demonstrated the effectiveness the proposed scheme by obtaining a Dice score of 96.94%, 97.93% for accuracy, 97.03% for recall, 96.81% for precision, 97.61% for specificity and 93.52% for Jaccard Coefficient Score, which are significantly superior to the recent state-of-the-art methods for individual tooth identification and segmentation.