CenterFormer: A Novel Cluster Center Enhanced Transformer for Unconstrained Dental Plaque Segmentation

Abstract

Dental plaque segmentation is crucial for maintaining oral health. However, accurately segmenting dental plaque in unconstrained environments can be challenging due to its low contrast and high variability in appearance. While existing transformer-based networks rely on attention mechanisms for each pixel, they do not take into account the relationships between neighboring pixels. Consequently, feature extraction is limited, making it difficult to achieve accurate segmentation of low-contrast images. To address this issue, we propose a simple yet efficient cluster center transformer that improves dental plaque segmentation by clustering image pixels based on multiple levels of feature maps' intensity and texture information. By grouping similar pixels into regions, the proposed method enables the transformers to focus on the local contour and edge around the teeth regions, adapting to the low contrast and high variability of plaque appearance, leading to more accurate and efficient segmentation of dental plaque in dental images. Additionally, we designed Multiple Granularity Perceptions using a pyramid fusion mechanism to capture multiple scales of vision features, thereby enhancing the low-contrast vision features. The proposed method can benefit the dental diagnosis and treatment planning process by improving the accuracy and efficiency of dental plaque segmentation. Our proposed method achieved state-of-the-art results on the dental plaque dataset (Li et al., 2020), with intersection over union (IoU) of 60.91% and pixel accuracy (PA) of 76.81%, all of which were the highest among all methods, demonstrating its effectiveness in plaque segmentation in unconstrained environments.