Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge

arXiv:2404.01013v1 Announce Type: new
Abstract: 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 …

abstract anthropic arxiv cs.ai cs.cv dental diagnostics differences framework frameworks general health however images instance knowledge labeling localization modern planning population prior segmentation studies treatment type