  Teeth localization, segmentation, and labeling in 2D images have great
potential in modern dentistry to enhance dental diagnostics, treatment
planning, and population-based studies on oral health. However, general
instance segmentation frameworks are incompetent due to 1) the subtle
differences between some teeth' shapes (e.g., maxillary first premolar and
second premolar), 2) the teeth's position and shape variation across subjects,
and 3) the presence of abnormalities in the dentition (e.g., caries and
edentulism). To address these problems, we propose a ViT-based framework named
TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an
Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two
modules, we design 1) a unique permutation-based upscaler to ensure high
efficiency while establishing clear segmentation boundaries with 2) multi-head
self/cross-gating layers to emphasize particular semantics meanwhile
maintaining the divergence between token embeddings. Besides, we collect 3) the
first open-sourced intraoral image dataset IO150K, which comprises over 150k
intraoral photos, and all photos are annotated by orthodontists using a
human-machine hybrid algorithm. Experiments on IO150K demonstrate that our
TeethSEG outperforms the state-of-the-art segmentation models on dental image
segmentation.
