ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction

• URL: http://arxiv.org/abs/2605.07082v1
• Date: Fri, 08 May 2026 01:02:36 GMT
• Title: ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction
• Abstract summary: Implant region itself is often characterized by a lack of distinctive texture in medical images.<n>We propose ImplantMamba, a network architecture designed for long-range sequential modeling.<n>Our approach explicitly couples the regression of the implant position with its slope.
• Score: 40.067766411250055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the design of surgical guides for implant placement, determining the precise implant position is a critical step. However, the implant region itself is often characterized by a lack of distinctive texture in medical images. Consequently, artificial intelligence (AI) models must infer the correct implant position and angulation (slope) primarily by analyzing the texture of the surrounding teeth, which poses a significant challenge. To address this, we propose ImplantMamba, a network architecture designed for long-range sequential modeling to integrate texture information from adjacent teeth. Our approach explicitly couples the regression of the implant position with its slope. The core of ImplantMamba is a hybrid encoder that combines Convolutional Neural Networks (CNNs) with Mamba layers. This design enables the network to hierarchically extract local anatomical features through CNNs while simultaneously modeling global contextual dependencies across the entire scan volume via Mamba's selective scan operations, leading to a more comprehensive understanding of the implant site. Furthermore, we introduce a Slope-Coupled Prediction Branch (SCP). This branch is designed to connect the prediction of implant position with the slope, ensuring internal consistency and anatomical plausibility by thereby enforcing a coherent relationship between the predicted implant location and its angulation. Extensive experiments on a large-scale dental implant dataset demonstrate that the proposed ImplantMamba achieves superior performance compared to existing methods.

Related papers

• Towards Voxel Spacing Consistency for Medical Image Segmentation [56.992050146446495]
  We study the correlation between image resampling and segmentation, and propose Consispace, a semantic-aware resampling framework.<n> Consispace achieves consistent voxel spacing in the axial direction while preserving anatomical and semantic consistency.
  arXiv  Detail & Related papers  (2026-06-30T15:42:23Z)
• Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design [40.12466032497699]
  TEMAD is a fully automated, text-conditioned multi-expert architecture for multi-abutment design.<n>This framework integrates implant site localization and implant system, compatible abutment parameter regression into a unified pipeline.<n> experiments on a large-scale abutment design dataset show that TEMAD achieves state-of-the-art performance compared to existing methods.
  arXiv  Detail & Related papers  (2026-04-10T07:14:36Z)
• RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning [45.095689544963136]
  Existing methods have to scan the post-implantation data and map the implant to pre-implantation space to get the label of implant position for training.<n>We propose to mask the implants in the post-implantation data so that any CBCT containing the implants can be used as training data.<n>On this basis, we proposes ImplantFairy, a comprehensive, publicly accessible dental implant dataset with voxel-level 3D annotations of 1622 CBCT data.
  arXiv  Detail & Related papers  (2026-01-21T06:30:18Z)
• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction [27.020346431680355]
  In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. Deep neural network has been proposed to assist the dentist in locating the implant position. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT)
  arXiv  Detail & Related papers  (2023-08-10T05:51:21Z)
• Two-Stream Regression Network for Dental Implant Position Prediction [29.864052654136614]
  In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position. Deep learning based methods has started to be applied to address this problem. The space between teeth are various and some of them might present similar texture characteristic with the actual implant region.
  arXiv  Detail & Related papers  (2023-05-17T08:37:26Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data [27.020346431680355]
  Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root.
  arXiv  Detail & Related papers  (2022-10-29T02:31:27Z)
• Orientation-guided Graph Convolutional Network for Bone Surface Segmentation [51.51690515362261]
  We propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. Our approach improves over the state-of-the-art methods by 5.01% in connectivity metric.
  arXiv  Detail & Related papers  (2022-06-16T23:01:29Z)
• Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans [56.55092443401416]
  emphiMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.953pm0.076$, significantly outperforming the original MeshSegNet. PointNet-Reg achieved a mean absolute error (MAE) of $0.623pm0.718, mm$ in distances between the prediction and ground truth for $44$ landmarks, which is superior compared with other networks for landmark detection.
  arXiv  Detail & Related papers  (2021-09-24T13:00:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.