ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

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. However, such design heavily relies on the subjective experiences of dentists. 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. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods

Two-Stream Regression Network for Dental Implant Position Prediction

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. 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

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.Comment: MICCAI 202

Surface-Based Structure-from-Motion Using Feature Groupings

In this paper we describe a complete system from feature extraction to reconstruction of 3D models of indoor environments. The system uses a novel matching algorithm which matches groupings of features associated with boundaries of objects in the scene. We also present an extension of our structure-from-motion algorithm to incorporate surface constraints. We describe how planar surfaces can be incorporated into the model update procedure, and are hypothesised from the matched groupings of features between image frames. We present reconstructions of environments taken by an autonomous robot to demonstrate the improvement that can be achieved by this approach

Assessing Environmental Control of Sap Flux of Three Tree Species Plantations in Degraded Hilly Lands in South China

Prerequisite for selection of appropriate tree species in afforestation programs is to understand their water use strategy. Acacia mangium Willd., Schima wallichii Choisy, and Cunninghamia lanceolata (Lamb.) Hook are the three main vegetation restoration pioneer species in southern China, but no comparative research on the water use strategy of these three tree species have been reported. Our objective was to gain a detailed understanding of how photosynthetically active radiation (PAR), vapor pressure deficit (VPD), and soil water content (SWC) at different soil depths control the sap flux density (Js) in the dry and wet seasons. We measured the Js of these three tree species by using the thermal dissipation method in low subtropical China. We found that both S. wallichii and C. lanceolata differed clearly in their stomatal behavior from one season to another, while A. mangium did not. The canopy conductance per sapwood area of S. wallichii and C. lanceolata was very sensitive to VPD in the dry season, but not in the wet season. The Js of A. mangium was negatively correlated to SWC in all soil layers and during both seasons, while the other two species were not sensitive to SWC in the deeper layers and only positively correlated to SWC in dry season. Our results demonstrate that the three species have distinct water use strategies and may therefore respond differently to changing climate

Assessing Environmental Control of Sap Flux of Three Tree Species Plantations in Degraded Hilly Lands in South China

Prerequisite for selection of appropriate tree species in afforestation programs is to understand their water use strategy. Acacia mangium Willd., Schima wallichii Choisy, and Cunninghamia lanceolata (Lamb.) Hook are the three main vegetation restoration pioneer species in southern China, but no comparative research on the water use strategy of these three tree species have been reported. Our objective was to gain a detailed understanding of how photosynthetically active radiation (PAR), vapor pressure deficit (VPD), and soil water content (SWC) at different soil depths control the sap flux density (Js) in the dry and wet seasons. We measured the Js of these three tree species by using the thermal dissipation method in low subtropical China. We found that both S. wallichii and C. lanceolata differed clearly in their stomatal behavior from one season to another, while A. mangium did not. The canopy conductance per sapwood area of S. wallichii and C. lanceolata was very sensitive to VPD in the dry season, but not in the wet season. The Js of A. mangium was negatively correlated to SWC in all soil layers and during both seasons, while the other two species were not sensitive to SWC in the deeper layers and only positively correlated to SWC in dry season. Our results demonstrate that the three species have distinct water use strategies and may therefore respond differently to changing climate

3D Shape Modelling through a Constrained Estimation of a Bicubic B-spline Surface

This paper presents a new method to extract the 3D shape of objects from 3D gray level images using a bicubic B-spline surface model. Extraction of object shape is achieved through a hierarchical surface fitting by exploiting the multi-scale representation of the model. A strategy for converting the surface estimation into curve estimations is devised. The model surface is estimated by successively computing a set of cubic B-spline curves consisting of a coordinate curve net defining the surface. A regularising component is incorporated into the curve estimation to encourage the generation of an orthogonal coordinate curve net, preventing the creation of unwanted creases. Experimental results are presented for extracting the 3D shape of objects from real 3D images. 1 Introduction The interpretation of 3D images often needs the shape information of objects in the image. A set of unmodelled 3D structures derived from local low level operations (e.g. edge detection) [10] can hardly ever ..