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

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…

• NN
  National Natural Science Foundation of China

  Awards: 62441201, 62272021, 62102036
• NS
  Natural Science Foundation of Beijing Municipality

  Awards: L232102, 4222024
• BS
  Beijing Science and Technology Planning Project

  Award: Z231100005923039