bibtext: @InProceedings{ Hua_IOSSAM_MICCAI2024,
                   author = { Huang, Xinrui and He, Dongming and Li, Zhenming and Zhang, Xiaofan and Wang, Xudong },
                   title = { { IOSSAM: Label Efficient Multi-View Prompt-Driven Tooth Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Segmenting and labeling teeth from 3D Intraoral Scans (IOS) plays a significant role in digital dentistry. Dedicated learning-based methods have shown impressive results, while they suffer from expensive point-wise annotations. We aim at IOS segmentation with only low-cost 2D bounding-boxes annotations in the occlusal view. To accomplish this objective, we propose a SAM-based multi-view prompt-driven IOS segmentation method (IOSSAM) which learns prompts to utilize the pre-trained shape knowledge embedded in the visual foundation model SAM. Specifically, our method introduces an occlusal prompter trained on a dataset with weak annotations to generate category-related prompts for the occlusal view segmentation. We further develop a dental crown prompter to produce reasonable prompts for the dental crown view segmentation by considering the crown length prior and the generated occlusal view segmentation. We carefully design a novel view-aware label diffusion strategy to lift 2D segmentation to 3D field. We validate our method on a real IOS dataset, and the results show that our method outperforms recent weakly-supervised methods and is even comparable with fully-supervised methods.
title: IOSSAM: Label Efficient Multi-View Prompt-Driven Tooth Segmentation
authors: Huang, Xinrui He, Dongming Li, Zhenming Zhang, Xiaofan Wang, Xudong