Guoli Song

Lung nodule detection remains a critical challenge in clinical diagnostics due to the small size, weak contrast, and high background interference of nodules in CT scans. To address these issues, a novel deep neural network architecture, termed Daisy-Net, is proposed. This model incorporates dual attention mechanisms and inter-scale feature perception, consisting of two primary components: the Parallelized Patch and Spatial Context Aware (PPSCA) module and the Omni-domain Multistage Fusion (OMF) module. The PPSCA module enhances the extraction of fine-grained textures and boundary information through multi-branch patch perception and spatial attention. The OMF module employs omni-domain feature fusion and progressive stage-wise supervision to improve robustness and discrimination under complex conditions. The lung nodule detection task is formulated as a two-dimensional segmentation problem and evaluated on the LUNA16 dataset. In the post-binarization comparative evaluation, Daisy-Net achieves the best overall performance among all compared methods, with an Intersection over Union (IoU) of 81.41, a Dice coefficient of 89.75, a precision of 95.34, a sensitivity of 84.78, and a specificity of 99.9974. These findings indicate the model’s strong capability in detecting small pulmonary nodules accurately and reliably.

Despite advancements in robotic surgery, recent developments in surgical robots have rarely focused on ensuring the safety of surgeons and other devices within the surgical environment. This paper introduces a novel real-time modeling and perception method tailored for surgical Dynamic Unstructured Environments, leveraging multisensory data. We propose the use of a uniform Cartesian grid (UCG) over the traditional radial non-uniform grid (RNUG) to establish a more accurate offline model of the surgical environment. Furthermore, our approach incorporates multisensory data fusion and UCGs for rapid online updates of environmental models. This dual strategy of offline modeling and online updating addresses both modeling accuracy and efficiency, making UCG modeling more suitable for multisensory fusion compared to RNUG. The proposed methodology enhances the overall safety and operational reliability of surgical robots.

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in simultaneously achieving both high-precision and high-speed requirements. To address this, this study proposes the real-time defect detection algorithm FDC-YOLO, enabling precise and efficient identification of blurred defects. We design the dynamic subtractive attention sampling module (DSAS) to dynamically compensate for information discrepancies during sampling, which reduces critical information loss caused by multi-scale feature fusion. We design a high-frequency information processing module (HFM) to enhance defect feature representation in the frequency domain, which significantly improves the visibility of defect regions while mitigating blur-induced noise interference. Additionally, we design a classification domain detection head (CDH) to focus on domain-invariant features across categories. Finally, FDC-YOLO achieves 7.9% and 3.5% mAP improvements on the aero-engine blade defect dataset and low-resolution NEU-DET dataset, respectively, with only 2.68 M parameters and 7.0G FLOPs. These results validate the algorithm’s generalizability in addressing low-accuracy issues across diverse blur artifacts in defect detection. Furthermore, this algorithm is combined with the tensegrity continuum robot to jointly construct an automatic defect detection system for aircraft engines, providing an efficient and reliable innovative solution to the problem of internal damage detection in engines.

Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications.

This study focuses on enhancing the precision and efficacy of pedicle screw placement in spinal surgeries, particularly for patients with osteoporosis. It emphasizes the importance of accurate screw positioning to maximize pullout strength and biomechanical efficacy. The research highlights the relationship between CT values, Bone Mineral Density (BMD), and the Young’s modulus of bone tissue, suggesting that higher CT values, indicative of denser bone, lead to stronger mechanical properties. This understanding is crucial for assessing bone health, especially in osteoporosis and fracture risk analysis. The study introduces an innovative automated planning method for pedicle screw insertion in spinal vertebrae, beneficial for osteoporotic patients. This method uses PointNet++ combined with a Siamese network for semi-supervised segmentation of vertebrae in CT images, converting them into point clouds for individualized planning. The approach aims to improve accuracy and efficiency in pedicle screw placement, especially in complex or osteoporotic vertebrae. The method was validated using osteoporotic in vitro models, demonstrating its potential effectiveness for surgeons facing challenges in pedicle screw placement in osteoporotic patients. The complete workflow begins with semi-supervised vertebra segmentation using a Siamese PointNet++ architecture. The extracted vertebra point cloud is then aligned with a predefined pedicle screw model via an adaptively tuned Super4PCS registration algorithm to generate patient-specific trajectories. The paper also reviews traditional surgical path planning, which relied on surgeons’ experience and intuition, and the shift towards Computer-Aided Design (CAD) and Virtual Reality (VR) technologies for pre-operative planning. Despite these advancements, challenges remain in accurately simulating tissue properties and managing physiological variations. The study proposes a bifurcated approach to automated trajectory planning: segmenting individual vertebrae and planning pedicle screw implantation for each segmented vertebra. The method involves converting CT scans into 3D point clouds, using PointNet++ and the Siamese method for vertebra segmentation, and registering the pedicle screw point cloud with the vertebra model for precise surgical planning. The study demonstrates the method’s feasibility through clinical data from Shengjing Hospital, showing high applicability and consistency in surgical path planning, particularly in the lumbar and lower thoracic regions. The planning results were consistent with surgeons’ experience, indicating the algorithm’s adaptability and stability. Note to Practitioners—Pedicle screw placement is a critical procedure in spinal surgery, especially for osteoporotic patients where achieving strong fixation is essential. Traditional methods depend heavily on the surgeon’s expertise and the use of intraoperative fluoroscopy, which can be both time-consuming and susceptible to human error. Recent advancements propose an automated computational approach that combines deep learning techniques, such as PointNet++ and Siamese networks, for semi-supervised vertebral segmentation. This method also employs an enhanced Super4PCS algorithm for registration-based trajectory planning, aiming to provide a more accurate, consistent, and efficient strategy for pedicle screw placement. This innovative approach offers several key benefits: Automated Vertebral Segmentation and Trajectory Planning: By reducing the need for manual intervention, this method delivers reliable and reproducible paths for pedicle screw insertion based on CT imaging. Enhanced Precision and Safety: By identifying optimal screw trajectories in high-density bone regions through CT value analysis, the approach maximizes pullout strength and minimizes the risk of vertebral perforation, which is particularly beneficial for osteoporotic patients. Clinical Validation and Feasibility: Validated using clinical data from Shengjing Hospital, the study demonstrated alignment with expert surgical decisions. The deviation from planned trajectories in actual surgeries was less than 1 degree, confirming high accuracy. Integrating this method into surgical navigation systems can assist orthopedic surgeons in preoperative planning and provide real-time intraoperative guidance. This integration can lead to minimized surgical risks and complications, reduced radiation exposure by decreasing fluoroscopy usage, and improved surgical workflow efficiency, making spinal surgeries faster and more predictable.

Background: Accurate identification of bone structures helps surgeons to better locate target areas during the procedure and reduce damage to surrounding tissue. To efficiently automate the laborious task of labeling vertebrae in computed tomography (CT) images, we propose a DBU-Net deep learning segmentation network built upon the nnUnet framework. Methods: Specifically, DBU-Net incorporates the multi-scale feature channel attention module and the dual-branch decoder architecture. The multi-scale feature channel attention module effectively combines image features from adjacent stages, integrating detailed and structural information from different scales. By applying a weighted operation, it adaptively adjusts the importance of each channel, enabling precise extraction of multi-scale features. The dual-branch decoder structure, built upon the nnUNet decoder, adds a branch that incorporates a contextual Transformer module to capture global contextual information. At each decoding stage, the features from the two branches interact, seamlessly merging global context with local features. This significantly enhances the network’s ability to process complex features in spinal CT images, improving segmentation accuracy. Results: We conducted a comprehensive evaluation using the Vertebrae Segmentation (VerSe) dataset from MICCAI 2019 and 2020. The experimental results demonstrate that DBU-Net achieves state-of-the-art performance with an average Dice coefficient of 94.59%. Conclusions: This highlights its significant potential to assist surgeons in clearly and accurately identifying and locating spinal structures, and it is anticipated to provide robust technical support for the precise execution of spine-related surgeries and the effective diagnosis of diseases.

In robotic flexible ureteroscopy, great care should be taken to prevent excessive collision force at the ureteroscope tip to avoid urinary tissue damage. However, the passive flexure of a flexible ureteroscope from reaction forces with the surrounding tissue introduces great uncertainties in force control. To address this, a probability-based force control method is proposed in this paper. A morphological wavelet-based statistic test is proposed to detect collision by identifying the change point of the probability distribution of force signal, which is measured by a fiber optical sensor at the ureteroscope tip. The force signal and its change points are applied as an admittance model inputs to generate position command. It is augmented to the robot controller for minimizing the collision force. Meanwhile, a probabilistic model approximates the real interactive system, and combines Bayesian inference for safely online learning the admittance parameters. Experimental results on a flexible ureteroscopy robot show that collision can be detected instantly, and the force is reduced remarkably during the advancement of the robotic ureteroscope. Note to Practitioners —This paper is motivated by the rapidly increasing applications for robotic flexible ureteroscopy. As ureteroscope enters into urethra, urinary bladder, ureter, and reaches renal pelvis, it sometimes penetrates soft tissue, especially in the case of no ureteral access sheath placement. It is important to control the ureteroscope tip force for surgery safety. Therefore, a force control method is proposed in this paper. Experiments conducted on a flexible ureteroscopy robot demonstrate that the proposed method is a protective measure to mitigate the risk of inner surface perforation.

2D X-ray images are extensively employed for intraoperative navigation and localization owing to their high imaging efficiency, low radiation risk, and affordability. However, this method can only yield overlapped anatomical information from a restricted number of projected views. Conversely, intraoperative CT scanning techniques, offering 3D images, elevate the risk of radiation exposure for both patients and healthcare professionals. For this purpose, we propose a V-shaped convolutional attention mechanism network (X-CTCANet) designed for X-ray reconstruction of CT images. The network enhances reconstruction performance by promoting task consistency in encoding-decoding, minimizing semantic differences between feature mappings. Additionally, it introduces an adaptive convolutional channel attention (CCA) mechanism to compel the network to prioritize essential feature regions. Experimental results demonstrate the successful CT image reconstruction from spine X-rays using X-CTCANet, achieving an SSIM value of 0.805 and a PSNR value of 34.64 dB. This underscores the considerable potential of accurate 3D CT reconstruction from 2D X-ray images in offering image support for surgical robots.