Titlebook: Semi-supervised Tooth Segmentation; First MICCAI Challen Yaqi Wang,Xiaodiao Chen,Hongyuan Zhang Conference proceedings 2025 The Editor(s) (

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,MsNet: Multi-stage Learning from Seldom Labeled Data for 3D Tooth Segmentation in Dental Cone Beam ysis. However, due to variations in dental anatomy, different imaging protocols, and limitations in accessing public datasets, developing an automated algorithm for dental analysis is challenging. This paper introduces a multi-stage learning-based method, named MsNet, utilizing a small amount of lab

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,Self-training Based Semi-Supervised Learning and U-Net with Denoiser for Teeth Segmentation in X-Rasignificance to effectively use the unlabeled images to improve the segmentation performance. In order to make full use of unlabeled data, in this paper, we design a three-stage pseudo-label training framework based on self-training to improve the pseudo-label quality in a progressive way. Hard data

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,UX-CNet: Effective Edge Information Acquisition for Teeth Image Segmentation,and difficulty in accurately segmenting the detailed information of the edges of the teeth. In this paper, we design a deep learning algorithm named . that can effectively segment teeth and solve the problem of poor effect of teeth edge segmentation. In the experiment, data augmentation was performe

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,2D Teeth Segmentation Base on Half-Image Approach and VCMix-Net+, and education. However, the annotations made by radiologists may be subjective, and manual annotation requires a considerable amount of time and labor costs. In this paper, we propose deep-learning model VCMix-Net+ to achieve high-quality segmentation of 2D teeth images. Our VCMix-Net+ performs par

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