Tooth segmentation is an essential step in digital dental diagnosis and treatment planning, playing a crucial role across various dental specialties. Despite its importance, this process is fraught with challenges due to the high noise and low contrast inherent in 2D and 3D dental radiographic images. Both convolutional neural networks (CNNs) and transformers have shown promise in tooth image segmentation, yet each method has limitations in handling long-range dependencies and computational complexity. To address this issue, this paper introduces T-Mamba, integrating frequency-based features and shared bi-positional encoding into vision mamba to address limitations in modeling global features efficiently. Besides, we designed a gate selection unit to integrate two features in the spatial domain and one feature in the frequency domain adaptively. T-Mamba is the first to integrate frequency-based features into vision mamba, offering the flexibility to process both 2D and 3D tooth image data without the need for separate modules. Additionally, the TED, a large-scale public 2D dental X-ray dataset, has been presented in this paper. Extensive experiments demonstrated that T-Mamba achieved new SOTA performance on both the 3D dental CBCT dataset and our proposed 2D TED dataset. The code and models are publicly available athttps://github.com/isbrycee/T-Mamba.

Recent advances in large vision-language models (LVLMs) have demonstrated strong performance on general-purpose medical tasks. However, their effectiveness in specialized domains such as dentistry remains underexplored. In particular, panoramic X-rays, a widely used imaging modality in oral radiology, pose interpretative challenges due to dense anatomical structures and subtle pathological cues, which are not captured by existing medical benchmarks or instruction datasets. To this end, we introduce MMOral, the first large-scale multimodal instruction dataset and benchmark tailored for panoramic X-ray interpretation. MMOral consists of 20,563 annotated images paired with 1.3 million instruction-following instances across diverse task types, including attribute extraction, report generation, visual question answering, and image-grounded dialogue. In addition, we present MMOral-Bench, a comprehensive evaluation suite covering five key diagnostic dimensions in dentistry. We evaluate 64 LVLMs on MMOral-Bench and find that even the best-performing model, i.e., GPT-4o, only achieves 43.31% accuracy, revealing significant limitations of current models in this domain. To promote the progress of this specific domain, we provide the supervised fine-tuning (SFT) process utilizing our meticulously curated MMOral instruction dataset. Remarkably, a single epoch of SFT yields substantial performance enhancements for LVLMs, e.g., Qwen2.5-VL-7B demonstrates a 24.73% improvement. MMOral holds significant potential as a critical foundation for intelligent dentistry and enables more clinically impactful multimodal AI systems in the dental field.

Detecting glass regions is a challenging task due to the inherent ambiguity in their transparency and reflective characteristics. Current solutions in this field remain rooted in conventional deep learning paradigms, requiring the construction of annotated datasets and the design of network architectures. However, the evident drawback with these mainstream solutions lies in the time-consuming and labor-intensive process of curating datasets, alongside the increasing complexity of model structures. In this paper, we propose to address these issues by fully harnessing the capabilities of two existing vision foundation models (VFMs): Stable Diffusion and Segment Anything Model (SAM). Firstly, we construct a Synthetic but photorealistic large-scale Glass Surface Detection dataset, dubbed S-GSD, without any labour cost via Stable Diffusion. This dataset consists of four different scales, consisting of 168 k images totally with precise masks. Besides, based on the powerful segmentation ability of SAM, we devise a simpleGlass surface sEgMentor named GEM, which follows the simple query-based encoder-decoder architecture. Comprehensive experiments are conducted on the large-scale glass segmentation dataset GSD-S. Our GEM establishes a new state-of-the-art performance with the help of these two VFMs, surpassing the best-reported method GlassSemNet with an IoU improvement of 2.1%. Additionally, extensive experiments demonstrate that our synthetic dataset S-GSD exhibits remarkable performance in zero-shot and transfer learning settings.