Semantic Consistency Network with Edge Learner and Connectivity Enhancer for Cervical Tumor Segmentation from Histopathology Images.

Accurate tumor grading and regional identification of cervical tumors are important for diagnosis and prognosis. Traditional manual microscopy methods suffer from time-consuming, labor-intensive, and subjective bias problems, so tumor segmentation methods based on deep learning are gradually becoming a hotspot in current research. Cervical tumors have diverse morphologies, which leads to low similarity between the mask edge and ground-truth edge of existing semantic segmentation models.

A Brief Introduction to Intelligent Point Cloud Processing, Sensing, and Understanding: Part II.

Point cloud, which represents the three-dimensional (3D) digital world, is one of the fundamental data carriers in many emerging applications [...

[Evaluation of the accuracy of three-dimensional data acquisition from liquid- interference surfaces assisted by a scanner head with a compressed airflow system].

Objective: To quantitatively evaluate the accuracy of data obtained from liquid-interference surfaces using an intraoral 3D scanner (IOS) integrated with a compressed airflow system, so as to provide clinical proof of accuracy for the application of the compressed airflow system-based scanning head in improving data quality on liquid-interference surfaces.

Methods: The study selected a standard model as the scanning object, adhering to the "YY/T 1818-2022 Dental Science Intraoral Digital Impression Scanner" guidelines, a standard that defined parameters for intraoral scanning. To establish a baseline for accuracy, the ATOS Q 12M scanner, known for its high precision, was used to generate true reference values.

Template-aided and freehand guiding plane preparation for removable partial dentures: A randomized controlled trial.

Purpose: To evaluate the accuracy of guiding plane preparation for removable partial dentures (RPDs) using 3D-printed templates compared to the freehand method.

Materials And Methods: Twenty partially edentulous patients requiring RPDs were randomly divided into two groups: the template-aided group (n = 10) and the freehand group (n = 10). Fifty-six guiding planes were prepared by a single clinician using two different methods.

Efficient complete denture metal base design via a dental feature-driven segmentation network.

Background And Objective: Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy.

Methods: To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients.

Quantitative evaluation of the proximal contact area gap change characterization under intercuspal occlusion by intraoral 3D scanning: Food impaction with tight proximal contact.

Objective: This study aimed to present three indicators that represent the proximal contact area gap change under intercuspal occlusion and to see if and how these indicators influence food impaction with tight proximal contact.

Materials And Methods: Ninety volunteers were recruited for bite force measurement and intraoral scanning. Three-dimensional surface data and buccal bite data were obtained for 60 impacted and 60 non-impacted teeth.

LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics.

Intelligent Point Cloud Processing, Sensing, and Understanding.

Point clouds are considered one of the fundamental pillars for representing the 3D digital landscape [...

Accuracy of digital implant impressions using a novel structured light scanning system assisted by a planar mirror in the edentulous maxilla: An in vitro study.

Objectives: This study aimed to develop a structured light scanning system with a planar mirror to enhance the digital full-arch implant impression accuracy and to compare it with photogrammetry and intraoral scanner methods.

Materials And Methods: An edentulous maxillary stone cast with six scan bodies was scanned as the reference model using a laboratory scanner. Three scanning modalities were compared (n = 10): (1) self-developed structured light scanning with a mirror (SSLS); (2) intraoral scanner (IOS); and (3) photogrammetry system (PG).

TranSDFNet: Transformer-Based Truncated Signed Distance Fields for the Shape Design of Removable Partial Denture Clasps.

The ever-growing aging population has led to an increasing need for removable partial dentures (RPDs) since they are typically the least expensive treatment options for partial edentulism. However, the digital design of RPDs remains challenging for dental technicians due to the variety of partially edentulous scenarios and complex combinations of denture components. To accelerate the design of RPDs, we propose a U-shape network incorporated with Transformer blocks to automatically generate RPD clasps, one of the most frequently used RPD components.

The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance.

Purpose: The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung.

Mechanical properties of multilayer combined gradient cellular structure and its application in the WLL.

In order to improve the buffering performance of a walkable lunar lander (WLL), an optimization method of multi-layer combined gradient cellular structure (MCGCS) is proposed. The impact load, impact action time, impact overload, and deformation amount are analyzed. The buffering performance of the material is evaluated and verified effectively with the simulation data.

Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention.

Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the clinical diagnosis, mainly because of the commonly used low-magnification pathological images lack fine cellular structure information and accurate localization, the diagnosis results of pathologists are different from those of attentional convolutional network -based methods, and the gradient-weighted class activation mapping method cannot be optimized to create the best visualization map. To address this problem, we propose an end-to-end depth domain adaptive network (DDANet) with integration gradient CAM and priori experience-guided attention to improve the tumor grading performance and interpretability by introducing the pathologist's a priori experience in high-magnification into the depth model. Specifically, a novel priori experience-guided attention (PE-GA) method is developed to solve the traditional unsupervised attention optimization problem.

A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images.

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks.

ASI-DBNet: An Adaptive Sparse Interactive ResNet-Vision Transformer Dual-Branch Network for the Grading of Brain Cancer Histopathological Images.

Brain cancer is the deadliest cancer that occurs in the brain and central nervous system, and rapid and precise grading is essential to reduce patient suffering and improve survival. Traditional convolutional neural network (CNN)-based computer-aided diagnosis algorithms cannot fully utilize the global information of pathology images, and the recently popular vision transformer (ViT) model does not focus enough on the local details of pathology images, both of which lead to a lack of precision in the focus of the model and a lack of accuracy in the grading of brain cancer. To solve this problem, we propose an adaptive sparse interaction ResNet-ViT dual-branch network (ASI-DBNet).

A Dual Discriminator Adversarial Learning Approach for Dental Occlusal Surface Reconstruction.

Objective: Restoring the correct masticatory function of partially edentulous patient is a challenging task primarily due to the complex tooth morphology between individuals. Although some deep learning-based approaches have been proposed for dental restorations, most of them do not consider the influence of dental biological characteristics for the occlusal surface reconstruction. In this article, we propose a novel dual discriminator adversarial learning network to address these challenges.

Principal stress lines based design method of lightweight and low vibration amplitude gear web.

A lightweight and low vibration amplitude web design method was investigated to reduce gear weight and noise. It was based upon the relationship between length and orthogonality that the principal stress lines were designed at the gear web. By constructing a vibration control model with gear design parameters, the optimal distance was calculated.

DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-Stage Generative Adversarial Networks.

Restoring the correct masticatory function of broken teeth is the basis of dental crown prosthesis rehabilitation. However, it is a challenging task primarily due to the complex and personalized morphology of the occlusal surface. In this article, we address this problem by designing a new two-stage generative adversarial network (GAN) to reconstruct a dental crown surface in the data-driven perspective.

Efficient Computer-Aided Design of Dental Inlay Restoration: A Deep Adversarial Framework.

Restoring the normal masticatory function of broken teeth is a challenging task primarily due to the defect location and size of a patient's teeth. In recent years, although some representative image-to-image transformation methods (e.g.

Accuracy of a Novel Virtual Articulator for Recording Three-Dimensional Dentition.

Purpose: To research and develop a novel virtual articulator system (the PN-300) based on computer binocular vision, raster scanning, and simulation technology and to conduct a preliminary evaluation of its accuracy.

Materials And Methods: Two digital cameras were used to build the trajectory-tracking part of the virtual articulator system, and cameras combined with a projection module were used to form the scanning part of the system. The most prominent feature of the PN-300 is its ability to simultaneously obtain the 3D data of the subject's teeth and the movement trajectory of the mandible relative to the maxilla.

Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks.

The tooth defect is a frequently occurring disease within the field of dental clinic. However, the traditional manual restoration for the defective tooth needs an especially long treatment time, and dental computer aided design and manufacture (CAD/CAM) systems fail to restore the personalized anatomical features of natural teeth. Aiming to address the shortcomings of existed methods, this article proposes an intelligent network model for designing tooth crown surface based on conditional generative adversarial networks.

The extraction method of tooth preparation margin line based on S-Octree CNN.

The tooth preparation margin line has a significant impact on the marginal fitness for dental restoration. Among the previous methods, the extraction of margin line mainly relies on manual interaction, which is complicated and inefficient. Therefore, we propose a method to extract the margin line with the convolutional neural network based on sparse octree (S-Octree) structure.

Relative trajectory-driven virtual dynamic occlusal adjustment for dental restorations.

The abnormal occlusal contact can disrupt the coordination and health of the oral jaw system. Therefore, the dynamic adjustment of the occlusal surface is of great significance for assessing the status of occlusal contact and clarifying jaw factors of stomatognathic system diseases. To solve this problem, a trajectory subtraction algorithm based on screw theory to improve the accuracy of the occlusal movement trajectory is proposed in our paper.