Segmentation of the pulmonary vessel from computed tomography (CT) images plays a crucial role in the diagnosis and treatment of various lung diseases. Although deep learning-based approaches have shown remarkable progress in recent years, their performance is often hindered by the lack of high-quality annotated datasets, in which the complex anatomy and morphology of pulmonary vessels make manual annotation challenging, time-consuming, and prone to errors. To address this, we propose PV25, the first dataset that features finely paired annotations of both pulmonary vessels and airways. Moreover, we propose TPNet, a novel tubular-aware prompt-tuning framework for pulmonary vessel segmentation under few-shot training with limited annotations. Specifically, based on an advanced and frozen segmentation backbone, TPNet proposes tunable encoding and decoding networks that learn tubular structures as transfer learning priors, bridging the gap between the source and target pulmonary vessel domains. Specifically, TPNet is built in an encoder-decoder manner, including the fixed segmentation backbone, tunable encoding and decoding networks. In encoding stage, the Morphology-Driven Region Growing (MDRG) module is developed to leverage the tubular connectivity of vessels to guide the network in capturing fine-grained features of pulmonary vessels. In decoding stage, the Cross-Correlation Guidance (CCG) module is introduced to integrate multi-scale correlations between airway and vessel structures in a coarse-to-fine manner. Extensive experiments conducted on multiple datasets demonstrate that TPNet achieves state-of-the-art performance in pulmonary vessel segmentation under limited training data. Besides, TPNet shows strong performance in related tasks such as airway segmentation and artery-vein classification, highlighting its robustness and versatility.

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. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

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. Unlike existing dental restoration design algorithms, we introduce the voxel-based truncated signed distance field (TSDF) as an intermediate representation, which reduces the sensitivity of the network to resolution and contributes to more smooth reconstruction. Besides, a selective insertion scheme is proposed for solving the memory issue caused by Transformer blocks and enables the algorithm to work well in scenarios with insufficient data. We further design two weighted loss functions to filter out the noisy signals generated from the zero-gradient areas in TSDF. Ablation and comparison studies demonstrate that our algorithm outperforms state-of-the-art reconstruction methods by a large margin and can serve as an intelligent auxiliary in denture design.

From the perspective of collaborative innovation risk, the relationship between different risks and IP is explored based on the BPNN (Back Propagation Neural Network) model. Then, the SE (Synergy Effect) and the DC (Dynamic Capability) are introduced as intermediary and moderating variables. Following specific enterprise data input, the relationship between collaborative innovation risk and IP is analyzed based on deep learning and its endogenous mechanism. Conclusion: the analysis model of enterprise IP based on deep learning BPNN can well process enterprise data; different types of collaborative innovation risks in industrial parks have significantly negative effects on IP; the negative effect of organizational collaborative risk on IP is −0.268; apart from market risk factors, the other collaborative innovation methods further hinder the improvement of IP by inhibiting SE; apart from the risk of benefit distribution, the other collaborative innovation risks and IP are negatively regulated by DC, and the regulation effect on the risk of innovative factor input is the largest.

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. Specifically, in the first stage, a conditional GAN (CGAN) is designed to learn the inherent relationship between the defective tooth and the target crown, which can solve the problem of the occlusal relationship restoration. In the second stage, an improved CGAN is further devised by considering an occlusal groove parsing network (GroNet) and an occlusal fingerprint constraint to enforce the generator to enrich the functional characteristics of the occlusal surface. Experimental results demonstrate that the proposed framework significantly outperforms the state-of-the-art deep learning methods in functional occlusal surface reconstruction using a real-world patient database. Moreover, the standard deviation (SD) and root mean square (RMS) between the generated occlusal surface and the target crown calculated by our method are both less than 0.161 mm. Importantly, the designed dental crown have enough anatomical morphology and higher clinical applicability.

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. Pix2Pix) can be potentially applicable to restore the missing crown surface, most of them fail to generate dental inlay surface with realistic crown details (e.g. occlusal groove) that are critical to the restoration of defective teeth with varying shapes. In this article, we design a computer-aided Deep Adversarial-driven dental Inlay reStoration (DAIS) framework to automatically reconstruct a realistic surface for a defective tooth. Specifically, DAIS consists of a Wasserstein generative adversarial network (WGAN) with a specially designed loss measurement, and a new local-global discriminator mechanism. The local discriminator focuses on missing regions to ensure the local consistency of a generated occlusal surface, while the global discriminator aims at defective teeth and adjacent teeth to assess if it is coherent as a whole. Experimental results demonstrate that DAIS is highly efficient to deal with a large area of missing teeth in arbitrary shapes and generate realistic occlusal surface completion. Moreover, the designed watertight inlay prostheses have enough anatomical morphology, thus providing higher clinical applicability compared with more state-of-the-art methods.

In this paper, we proposed a 3D path planning method for a miniature robotic system, which can manipulate a beam of ultra-short pulse laser to cut a decayed tooth to formulate an expected 3D shape. Using high resolution STereo Lithography (STL) models of the original decayed tooth and the target preparing shape as the input, our method consists of a fast slicing algorithm and an optimized path generating algorithm, which realized a high efficient layer-by-layer cutting for laser ablation. Theoretical analysis on the geometric distortion and surface roughness was carried out to model the influence of the path planning algorithms on the accuracy of the prepared tooth. Experimental results on a real tooth indicate that the path planning method can maintain the accuracy for the laser ablation process.

As a preliminary step to achieve a long-term goal of developing an automatic dental preparation system for clinical operations, we design and build a miniature robotic system which can manipulate a laser beam to move in three dimensional spaces to remove hard tissue from a target tooth. The dental preparation requires the robotic system to own high accuracy, high ablation speed and small size. A 2D galvanometer scanners module is integrated to meet the requirement of a high moving speed of the laser focus. A closed-loop system based on a miniature-sized voice-coil motor and a grating ruler are developed to realize the accurate control of the focus. The overall size of the developed prototype is 108mm×56mm×43mm, which is small enough to be used in close proximity to a patient's mouth. The prototype has been tested by using two different kinds of laser generators, i.e., a nanosecond laser and a picosecond laser. The experiment results show that the robotic system can provide high moving speed of 1000mm/s with good shape accuracy. From the results, we found that nanosecond laser beam can be controlled to ablate zirconia and aluminum, but not suitable to ablate tooth because of tissue carbonization. By selecting suitable parameters of the picosecond laser generator, a target tooth could be ablated to produce a cylinder shape without carbonization. Limitations of the prototype are identified according to the experiment results.