# Two-Stream Regression Network for Dental Implant Position Prediction

arXiv: https://arxiv.org/abs/2305.10044 · HTML: https://mlsanity.com/paper/2305.10044/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 49 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 25 of 25)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [Mobile-Former: Bridging MobileNet and Transformer](https://mlsanity.com/paper/2108.05895/) (2021)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [Co-Scale Conv-Attentional Image Transformers](https://mlsanity.com/paper/2104.06399/) (2021)
- [MPViT: Multi-Path Vision Transformer for Dense Prediction](https://mlsanity.com/paper/2112.11010/) (2021)
- [Are Convolutional Neural Networks or Transformers more like human vision?](https://mlsanity.com/paper/2105.07197/) (2021)
- [A fully automated method for 3D individual tooth identification and segmentation in dental CBCT](https://mlsanity.com/paper/2102.06060/) (2021)
- [ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data](https://mlsanity.com/paper/2210.16467/) (2022)

## Built on by (most cited first, 11 of 11)

- [TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2308.05355/) (2023)
- [Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network](https://mlsanity.com/paper/2406.04603/) (2024)
- [SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis](https://mlsanity.com/paper/2406.13963/) (2024)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)
- [SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design](https://mlsanity.com/paper/2512.11507/) (2025)
- [Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction](https://mlsanity.com/paper/2406.12186/) (2024)
- [Text Condition Embedded Regression Network for Automated Dental Abutment Design](https://mlsanity.com/paper/2511.22578/) (2025)
- [Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design](https://mlsanity.com/paper/2604.09047/) (2026)
- [Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis](https://mlsanity.com/paper/2605.01741/) (2026)
- [ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction](https://mlsanity.com/paper/2605.07082/) (2026)
- [Structure-aware Knowledge-guided Heterogeneous Mamba for Zygomaticomaxillary Suture Assessment](https://mlsanity.com/paper/2606.16749/) (2026)

## Abstract (arXiv)

In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position, which is subjective and prone to doctor's experiences. When 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. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods.