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Abstract Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a significant health concern. The early detection of these plaques is crucial for targeted therapies and reducing the risk of cardiovascular diseases. This study presents PlaqueNet, a solution for segmenting coronary artery plaques from coronary computed tomography angiography (CCTA) images. For feature extraction, the advanced residual net module was utilized, which integrates a deepwise residual optimization module into network branches, enhances feature extraction capabilities, avoiding information loss, and addresses gradient issues during training. To improve segmentation accuracy, a depthwise atrous spatial pyramid pooling based on bicubic efficient channel attention (DASPP-BICECA) module is introduced. The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network’s information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.

AbstractThe aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People’s Hospital of Shenzhen between January 2017 and December 2022. The patients’ clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI −0.237 to 0.061, p

BackgroundNowadays, with the fast-increasing demand for neuro-endovascular therapy, surgeons in this field are in urgent need. Unfortunately, there is still no formal skill assessment in neuro-endovascular therapy in China.MethodsWe used a Delphi method to design a newly objective checklist for standards of cerebrovascular angiography in China and evaluated its validity and reliability. A total of 19 neuro-residents with no interventional experience and 19 neuro-endovascular surgeons from two centers (Guangzhou and Tianjin) were recruited; they were divided into two groups: residents and surgeons. Residents completed a simulation-based cerebrovascular angiography operation training before assessment. Assessments were under live and video record forms with two tools: the existing global rating scale (GRS) of endovascular performance and the new checklist.ResultsThe average scores of residents were significantly increased after training in two centers (p < 0.05). There is good consistency between GRS and the checklist (p = 0.856). Intra-rater reliability (Spearman's rho) of the checklist was >0.9, and the same result was also observed in raters between different centers and different assessment forms (p < 0.001, rho > 0.9). The reliability of the checklist was higher than that of the GRS (Kendall's harmonious coefficient is 0.849, while GRS is 0.684).ConclusionThe newly developed checklist appears reliable and valid for evaluating the technical performance of cerebral angiography and differentiating between trained and untrained trainees' performance well. For its efficiency, our method has been proven to be a feasible tool for resident angiography examination in certification nationwide.

ObjectiveToday, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D–3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images.MethodsTo make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D–3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm.ResultIn this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI).ConclusionThe experimental results in this study show that in the 2D–3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Abstract Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.

Abstract This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we reconstruct the patient-specific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors. The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE, which has the potential to be applied in clinical RFA guidance.

This paper presents a novel mixed reality (MR) guidance method for liver tumors radiofrequency ablation (RFA). Compared with traditional computed tomography (CT)-guided method, our system can provide a more natural and intuitive surgical mode for surgeons. In essence, our system is a holographic navigation platform, which projects a MR overlay onto the patient via HoloLens during RFA. We first reconstruct the patient-specific anatomy structure from the CT images of abdominal phantom. Then, a tailored precise registration method is employed to map the virtual-real spatial information. In addition, considering that tumor shifting during biopsy severely impacts the accuracy of RFA, our guidance system involves a motion compensation computation through data-driven physically-based modeling in holographic environment. In experiments, we conduct a user study on the comparison trial between MR-guided and CT-guided biopsy. User feedback demonstrates that our MR guidance method for needle placement procedure has the potential to simplify the operation, reduce the operation difficulty, shorten the operation time, and raise the operation precision.

Interactive progressive cutting of deformable body is vital for surgical training/planning systems with resection operations. Unfortunately, it is challenging to robustly simulate this phenomenon with faithful reflection of user's motion. The underlying reason is that sophisticated boundary conditions should be considered for the consistency between scalpel motion trajectory and incision during progressive cutting. Meanwhile, both geometrical and physical primitives along cutting trajectory need to be updated for each step induced by topological modifications, which is time-consuming for interactive environment. To tackle the above-mentioned issues, we present a phyxel-associated surface mesh approach for interactive progressive cutting of deformable bodies. Our method is based on a hybrid geometry, including a triangular surface mesh and an internal physical point cloud (phyxels), and we employ the moving least squares shape function to achieve the fast bijective mapping. This structure can well decouple the mechanical modeling from the geometry processing, thus allowing efficient handling of topological modifications, mechanical response, and rendering issues. Furthermore, we propose tailored continuous collision detection and constrained bilateral triangulation methods for fast geometrical modification. Besides, a dynamic adaptation scheme is proposed to avoid material slivers along splitting trajectories and guarantee numerical stability of simulation. Experimental results demonstrate that our method can provide effective progressive cutting simulation with realistic mechanical response, and it has a great potential to be an efficient and versatile computational framework for interactive surgical cutting simulation with high fidelity.

As a cutting-edge research topic in computer vision and graphics for decades, human skeleton extraction from single-depth camera remains challenging due to possibly occurring occlusions of different body parts, huge appearance variations, and sensor noise. In this paper, we propose to incorporate human skeleton length conservation and symmetry priors as well as temporal constraints to enhance the consistency and continuity for the estimated skeleton of a moving human body. Given an initial estimation of the skeleton joint positions provided per frame by the Kinect SDK or Nuitrack SDK, which do not follow the aforementioned priors and can prone to errors, our framework improves the accuracy of these pose estimates based on the length and symmetry constraints. In addition, our method is device-independent and can be integrated into skeleton extraction SDKs for refinement, allowing the detection of outliers within the initial joint location estimates and predicting new joint location estimates following the temporal observations. The experimental results demonstrate the effectiveness and robustness of our approach in several cases.

PurposeIn ultrasound-guided High Intensity Focused Ultrasound (HIFU) therapy, the target tissue (such as a tumor) often moves and/or deforms in response to an external force. This problem creates difficulties in treating patients and can lead to the destruction of normal tissue. In order to solve this problem, we present a novel method to model and predict the movement and deformation of the target tissue during ultrasound-guided HIFU therapy.MethodsOur method computationally predicts the position of the target tissue under external force. This prediction allows appropriate adjustments in the focal region during the application of HIFU so that the treatment head is kept aligned with the diseased tissue through the course of therapy. To accomplish this goal, we utilize the cow tissue as the experimental target tissue to collect spatial sequences of ultrasound images using the HIFU equipment. A Geodesic Localized Chan-Vese (GLCV) model is developed to segment the target tissue images. A 3D target tissue model is built based on the segmented results. A versatile particle framework is constructed based on Smoothed Particle Hydrodynamics (SPH) to model the movement and deformation of the target tissue. Further, an iterative parameter estimation algorithm is utilized to determine the essential parameters of the versatile particle framework. Finally, the versatile particle framework with the determined parameters is used to estimate the movement and deformation of the target tissue.ResultsTo validate our method, we compare the predicted contours with the ground truth contours. We found that the lowest, highest and average Dice Similarity Coefficient (DSC) values between predicted and ground truth contours were, respectively, 0.9615, 0.9770 and 0.9697.ConclusionOur experimental result indicates that the proposed method can effectively predict the dynamic contours of the moving and deforming tissue during ultrasound-guided HIFU therapy.