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In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users' Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%. [ABSTRACT FROM AUTHOR]

There are thousands or even tens of thousands of satellites in Low Earth Orbit (LEO). How to ensure the economic sustainability of LEO satellite constellation construction is an important issue currently. In this article, we envision integrating the popular and promising Internet of Things (IoT) technology with LEO satellite constellations to indirectly provide economic support for LEO satellite construction through paid IoT services. Of course, this can also bring benefits to the development of IoT. LEO Satellites can provide networks for IoT products in areas with difficult conditions, such as deserts, oceans, etc., and Satellite Edge Computing (SEC) can help to reduce the service latency of IoT. Many IoT products rely on Convolutional Neural Networks (CNNs) to provide services, and it is difficult to perform CNN inference on an edge server solely. Therefore, in this article, we use edge-distributed inference to enable the IoT services in the SEC scenario. How to perform edge-distributed inference to shorten inference time is a challenge. To shorten the inference latency of CNN, we propose a framework based on a joint partition, named EDIJP. We use a joint partition method combining data partition and model partition for distributed partition. We model the data partition as a Linear Programming (LP) problem. To address the challenge of trading off computation latency and communication latency, we designed an iterative algorithm to obtain the final partitioning result. By maintaining the original structure and parameters, our framework ensures that the inference accuracy will not be affected. We simulated the SEC environment, based on two popular CNN models, VGG16 and AlexNet, the performance of our method is varified. Compared with local inference, EdgeFlow, and CoEdge, the inference latency by using EDIJP is shorter. [ABSTRACT FROM AUTHOR]

Purpose: For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round. Design/methodology/approach: This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information. Findings: While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly. Originality/value: By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources. [ABSTRACT FROM AUTHOR]

The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed networks because network flow data are kept private. Existing methods cannot obtain an accurate NIDS under such a federated scenario. To construct an NIDS while preserving data privacy, we propose a combined model that integrates binary classifiers into a whole network based on simple classifier networks to specify the type of attack on anomalous data and offer instruction to other security system components. We also introduce federated learning (FL) methods into our system and design a new aggregation algorithm named vertical blocking aggregation (FedVB) according to our model structure. Our experiments demonstrate that our system can be more effective than simple multi-classifiers in terms of accuracy and significantly reduce communication and computation overhead when applying FedVB. [ABSTRACT FROM AUTHOR]

Featured Application: Increased parallelism in edge network security issues, Feature loss handling. Feature loss in IoT scenarios is a common problem. This situation poses a greater challenge in terms of real-time and accuracy for the security of intelligent edge computing systems, which also includes network security intrusion detection systems (NIDS). Losing some packet information can easily confuse NIDS and cause an oversight of security systems. We propose a novel network intrusion detection framework based on an improved neural network. The new framework uses 23 subframes and a mixer for multi-classification work, which improves the parallelism of NIDS and is more adaptable to edge networks. We also incorporate the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA) for feature selection, reducing parameters, communication, and memory overhead. We named the above system as Combinatorial Multi-Classification-NIDS (CM-NIDS). Experiments demonstrate that our framework can be more flexible in terms of the parameters of binary classification, has a fairly high accuracy in multi-classification, and is less affected by feature loss. [ABSTRACT FROM AUTHOR]

Optical character recognition (OCR) is the process of acquiring text and layout information through analysis and recognition of text data image files. It is also a process to identify the geometric location and orientation of the texts and their symmetrical behavior. It usually consists of two steps: text detection and text recognition. Scene text recognition is a subfield of OCR that focuses on processing text in natural scenes, such as streets, billboards, license plates, etc. Unlike traditional document category photographs, it is a challenging task to use computer technology to locate and read text information in natural scenes. Imaging sequence recognition is a longstanding subject of research in the field of computer vision. Great progress has been made in this field; however, most models struggled to recognize text in images of complex scenes with high accuracy. This paper proposes a new pattern of text recognition based on the convolutional recurrent neural network (CRNN) as a solution to address this issue. It combines real-time scene text detection with differentiable binarization (DBNet) for text detection and segmentation, text direction classifier, and the Retinex algorithm for image enhancement. To evaluate the effectiveness of the proposed method, we performed experimental analysis of the proposed algorithm, and carried out simulation on complex scene image data based on existing literature data and also on several real datasets designed for a variety of nonstationary environments. Experimental results demonstrated that our proposed model performed better than the baseline methods on three benchmark datasets and achieved on-par performance with other approaches on existing datasets. This model can solve the problem that CRNN cannot identify text in complex and multi-oriented text scenes. Furthermore, it outperforms the original CRNN model with higher accuracy across a wider variety of application scenarios. [ABSTRACT FROM AUTHOR]

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

Measuring deformation of vibrating specimens whose dimensions are in the submillimeter range introduces a number of difficulties using laser interferometry. Normal interferometry is not suitable because of a phase ambiguity problem. In addition, the noise effect is much more serious in the measurement of small objects because a high-magnification lens is used. We present a method for full-field measurement of displacement, velocity, and acceleration of a vibrating miniature object based on image-plane digital holographic microscopy. A miniature cantilever beam is excited by a piezoelectric transducer stage with a sinusoidal configuration. A sequence of digital holograms is captured using a high-speed digital holographic microscope. Windowed Fourier analysis is applied in the spatial and spatiotemporal domains to extract the displacement, velocity and acceleration. The result shows that a combination of image-plane digital holographic microscopy and windowed Fourier analyses can be used to study vibration without encountering a phase ambiguity problem, and one can obtain instantaneous kinematic parameters on each point. OCIS codes: 090.2880, 120.7280, 110.0180, 120.0120, 070.6020.

In this work, we aim at validating some soft tissue deformation models using high-resolution micro-computed tomography (Micro-CT) images. The imaging technique plays a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) for small force loads and provides good capabilities of creating accurate 3-D models of soft tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite-element models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remain a challenge. In this study, ex vivo lamb liver tissue is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We find that the LEM is more applicable to lamb liver than the LVEM for smaller force loads ( Index Terms--Finite-element (F.E.) model, hyperelastic, linear elastic, linear viscoelastic, liver tissue, Micro-CT.

Deep deterministic policy gradient (DDPG) algorithm is a reinforcement learning method, which has been widely used in UAV path planning. However, the critic network of DDPG is frequently updated in the training process. It leads to an inevitable overestimation problem and increases the training computational complexity. Therefore, this paper presents a multicritic-delayed DDPG method for solving the UAV path planning. It uses multicritic networks and delayed learning methods to reduce the overestimation problem of DDPG and adds noise to improve the robustness in the real environment. Moreover, a UAV mission platform is built to train and evaluate the effectiveness and robustness of the proposed method. Simulation results show that the proposed algorithm has a higher convergence speed, a better convergence effect, and stability. It indicates that UAV can learn more knowledge from the complex environment. [ABSTRACT FROM AUTHOR]

This article investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed by Marc Hallin and collaborators. In technical terms, the proposed test is consistent and distribution-free in the family of multivariate distributions with nonvanishing (Lebesgue) probability densities. Exploiting the (degenerate) U-statistic structure of the distance covariance and the combinatorial nature of Hallin's center-outward ranks and signs, we are able to derive the limiting null distribution of our test statistic. The resulting asymptotic approximation is accurate already for moderate sample sizes and makes the test implementable without requiring permutation. The limiting distribution is derived via a more general result that gives a new type of combinatorial noncentral limit theorem for double- and multiple-indexed permutation statistics. for this article are available online. [ABSTRACT FROM AUTHOR]

Objective. To predict the molecular mechanisms behind the benefits of Scutellaria barbata D. Don (S. barbata) in nasopharyngeal carcinoma (NPC) by network pharmacology and experimental verification. Methods. The active ingredients and targets of S. barbata were searched in the traditional Chinese medicine system pharmacology database and analysis platform, and the disease targets of NPC were obtained by searching the GeneCards database. A common target protein-protein interaction network was constructed by STRING, and then, an active ingredients-NPC-target interaction network map was constructed by Cytoscape 3.7.2 software. The functional enrichment analyses of Gene Ontology and KEGG pathway data were carried out by R software programming. Finally, cell proliferation was assessed by CCK8, apoptosis was detected by Annexin V-FITC/PI double fluorescence staining, and protein expression was analyzed by Western blotting. Results. In this study, 29 active ingredients were found in S. barbata. Among these, the main targets for NPC were baicalein, wogonin, luteolin, and quercetin. The main molecular targets of S. barbata on NPC were EGFR, MYC, CASP3, CCND1, and ESR1. The main biological processes involved the binding of DNA-binding transcription factors, RNA polymerase II-specific DNA-binding transcription factors, ubiquitin-like protein ligases, and ubiquitin-protein ligases. S. barbata mainly affects NPC through the PI3K-Akt, p53, and MAPK signaling pathways. The experimental results showed that baicalein and wogonin could inhibit proliferation and induce apoptosis of NPC cells and downregulate the expression of PI3K, AKT, and p53, the key proteins of the PI3K/AKT and p53 signaling pathway in CNE2 cells. Conclusion. Baicalein and wogonin, the main active ingredients of S. barbata, inhibited the proliferation and induced apoptosis of NPC cells through the PI3K/AKT and p53 signaling pathways. [ABSTRACT FROM AUTHOR]

To segment jaw structures in both open and closed mouth states from cone beam CT images and to preserve the true structures of trabecular bones. Segmentation algorithms of the mandible and maxilla were designed based on their different structures. We detected edges in volume and segment edges of the mandible and maxilla whether the mouth of a patient was open or closed. The internal structures of the mandible and maxilla were preserved by a morphological method with different parameters, respectively. An axial plane corresponding to the bottom of the hard palate was identified so that the bone structures of the maxilla above this plane were removed. Finally, the mandibular surface was smoothed by a simple thresholding method, and the maxillary surface was optimized by a geodesic active contours method. The 3D jaw model was segmented automatically by our proposed procedure with high accuracy. The average dice coefficients of the mandible and maxilla were equal to 0.9709 and 0.9420, respectively. The proposed jaw segmentation algorithm is automatic and knowledge-driven. The segmented jaws truly demonstrate the cortical and trabecular bone structures. It may potentially assist doctors in diagnosis and surgical planning. [ABSTRACT FROM AUTHOR]

The article discusses the concept of universal inference and its application in the split likelihood ratio test (SLRT). The authors explore the impact of nuisance parameters on the conservativeness of the SLRT and propose a modification to reduce this conservatism. They also compare their work to a related study that focuses on the large-sample theory for the SLRT. The two articles differ in their approach to improving power, with one focusing on the independence of the parameters and the other on tuning the data splitting ratio. The authors also investigate the power of the SLRT across different splitting ratios. Overall, the article provides insights into the application and limitations of universal inference in statistical testing. [Extracted from the article]

Some medical images such as blood vessels or nerve canal are very narrow, it is hardly segmented. In this paper, we propose a two-dimensional image segmentation algorithm combining K-means clustering, Gabor filter and moving mesh method. The mesh is moved adaptively concentrated in steep region and the location feature is added to our clustering algorithm. Moreover, the image feature values are obtained by the convolution operation between the image and Gabor filter. The advantage of this approach is that the segmentation result of the K-means clustering algorithm can be improved by constantly adding new features such as location, pixel or Gabor filter. The experiments indicate that our algorithms can quickly segment medical and crack images without compromising the quality of the results. The results demonstrated that the proposed method achieved precision 94.31%, recall 97.84%, F1 score 95.96%. [ABSTRACT FROM AUTHOR]

Objective To compare computed tomography arteriography (CTA) and digital subtraction arteriography (DSA) in the diagnosis of superior mesenteric artery dissecting aneurysm (SMADA). Methods All SMADA patients who underwent CTA and DSA at one of two medical centers between May, 2007 and April, 2017 were identified. The accuracy of CTA and DSA for the depiction of morphologic characteristics of SMADA was analyzed. Results Fourteen patients (12 men; mean age, 55.1 ± 6.4 years) were included in this study. The mean diameter of the dissecting aneurysm was 3.78 ± 1.53 mm on CTA and 3.81 ± 1.54 mm on DSA ( p = 0.96). The luminal stenosis was 0.52 ± 0.27 on CTA and 0.35 ± 0.23 on DSA ( p = 0.09). The thrombosed false lumen was visualized on CTA in 79% (11/14) of patients but in no patients on DSA ( p < 0.001). The entry points of the dissection were visualized on CTA in 64.3% (9/14) of patients and on DSA in 100% (14/14) of patients ( p = 0.041); CTA and DSA did not visualize re-entry points in any patients. The intimal flap was visualized on CTA in 71.4% (10/14) of patients and on DSA in 78.6% (11/14) of patients ( p > 0.05). Branch vessel involvement was visualized in 7.1% (1/14) of patients on CTA but in no patients on DSA ( p > 0.05). Conclusions CTA can be used in place of DSA for the diagnosis of SMADA. Although CTA may exaggerate the degree of luminal stenosis and is weak in depicting the entry points of SMADA, this modality more accurately depicts the thrombosed false lumen and branch vessel involvement. [ABSTRACT FROM AUTHOR]

Purpose:To evaluate the effects of transforming growth factor-β (TGF-β) on uterine fibroids of women of childbearing age and uterine artery embolization (UAE) using Embosphere microspheres. Material and methods:A total of 128 eligible women were randomly divided into an experimental group and a control group (n = 64) who received UAE using Embosphere microspheres and panhysterectomy, respectively. Another 128 healthy women receiving physical examination in the same period were also enrolled. Serum TGF-β levels were detected by enzyme-linked immunosorbent assay (ELISA). Serum TGF-β level, size of uterine fibroid and prognosis were followed up. Results:The serum TGF-β level of patients was significantly higher than that of healthy subjects. After treatment, the red blood cell counts and hemoglobin levels of the two patient groups significantly increased compared with those before (p < .05). After UAE, the diameter of uterine fibroids was significantly smaller than that before treatment, and the TGF-β level significantly decreased (p < .05). The expression of TGF-β in uterine fibroids was significantly higher than that in surrounding normal tissue (p < .05). Conclusion:With the uterus retained, the therapeutic effect of UAE was similar to that of panhysterectomy. TGF-β expression level was associated with growth of uterine fibroid in women of childbearing age, which can be used as a target for treatment. [ABSTRACT FROM PUBLISHER]

Abstract: The purpose of this paper is to validate simple linear elastic model and linear viscoelastic models for lamb liver tissue deformation using high resolution cone-beam CT images. The resolution of the scanned images is 5 to 25 times higher than typical CT scans. The tissue deformation details in the contact region between the tissue and the indentor can be detected and rendered for smaller force loads. For these loads, it is difficult to accurately predict the volume and surface changes using typical low resolution CT scans. Surgical simulations rely on the accurate representation of the mechanical response of the soft tissues which are subject to surgical manipulations. If the simulation results using a model are quite different from reality, it can be concluded that such model is not applicable to the modelling of the targeted tissue. From simulation results we compare the deformed tissue with that obtained from experiments to verify the applicability of the model to soft tissue deformation. For our validation, fresh ex vivo lamb liver is chosen to validate the linear elastic model and the linear viscoelastic model. [Copyright &y& Elsevier]

This paper proposes intensity and region-of-interest-based finite-element modeling of brain deformation. The modeling is based on the intensities of voxels within each element and motion analysis of region of interest (ROI) for image-guided minimally invasive surgery (MIS) applications. It is proposed to configure an ROI at the tip of surgical tools instead of the whole brain, so that the FEM is applied only on the ROI, and the maximum nodal displacement outside the ROI is small and in control. The deformed MR image using the proposed finite-element modeling shows better proportion to its original structures. [Copyright &y& Elsevier]

Storm drainage inlets transport urban runoff and discharge to underground sewer systems. If the inlet structure is blocked, the urban drainage system is hampered, leading to urban flooding. To quantitatively analyze the influence of clogging conditions on inlet discharge capacity, laboratory experiments were conducted to address the impact of different inlet clogging conditions on inlet discharge capacity under different upstream discharge conditions. These were based on a two-layer platform that mimicked a complete inlet structure including a drainage grate, a rainwater well, and a connecting pipe. The results show that the water flow near the inlet was similar to weir flow when the rainwater well was not full, whereas the water flow state near the inlet behaved similarly to orifice flow after becoming full. In addition, it was found that the clogging extent and position can significantly influence the comprehensive discharge capacity of the street inlet. The experimental dataset was used to calculate the inlet discharge coefficients of the weir and orifice flow states under different clogging conditions. The results are applicable to research addressing the formation mechanisms of urban floods. Additionally, this study is of practical significance for early warning systems and emergency response support during heavy rainfall. [ABSTRACT FROM AUTHOR]

Non-invasive blood glucose monitoring at microwave frequencies is generally thought to be unreliable in terms of reproducibility of measurements. The failure to reproduce a blood glucose measurement from one experiment to another is in major part due to the unwanted interaction of leaky waves between the ambient environment and the blood glucose measuring device. In this work, we have overcome this problem by simply eliminating the leaky modes through the use of surface electromagnetic waves from a curved Goubau line. In the proposed methodology, a fixed volume of blood-filled skin tissue was first formed by vacuum suction and partially wound with a curved Goubau line which was coated with a 3 mm thick layer of gelatin/glycerin composite. Blood glucose levels were non-invasively determined using a network analyzer. At 4.5 GHz, a near-linear correlation exists between the measured S12 parameters and the blood glucose levels. The measured correlation was highly reproducible and consistent with the measurements obtained using the conventional invasive lancing approach. The findings of this work suggest the feasibility of non-invasive detection of left and right imbalances in the body. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to improve the apparent motility of ocular prosthetic devices using technology. Prevailing ocular prostheses are acrylic shells with a static eye image rendered on the convex surface. A limited range of ocular prosthetic movement and lack of natural saccadic movements commonly causes the appearance of eye misalignment that may be disfiguring. Digital screens and computational systems may obviate current limitations in eye prosthetic motility and help prosthetic wearers feel less self-conscious about their appearance., Methods: We applied convoluted neural networks (CNNs) to track pupil location in various conditions. These algorithms were coupled to a microscreen digital prosthetic eye (DPE) prototype to assess the ability of the system to capture full ocular ductions and saccadic movements in a miniaturized, portable, and wearable system., Results: The CNNs captured pupil location with high accuracy. Pupil location data were transmitted to a miniature screen ocular prosthetic prototype that displayed a dynamic contralateral eye image. The transmission achieved a full range of ocular ductions and with grossly undetectable latency. Lack of iris and sclera color and detail, as well as constraints in luminosity, dimensionality and image stability limited the real eye appearance. Yet, the digitally rendered eye moved in the same amplitude and velocity as the native, tracked eye., Conclusions: Real-time image processing using CNNs coupled to microcameras and a miniscreen DPE may offer improvements in amplitude and velocity of apparent prosthetic eye movement. These developments, along with ocular image precision, may offer a next-generation eye prosthesis. NOTE: Publication of this article is sponsored by the American Ophthalmological Society., (Copyright © 2021 Elsevier Inc. All rights reserved.)

White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrated classical image processing and deep neural network for segmenting WMH from fluid attenuation inversion recovery (FLAIR) and T1-weighed magnetic resonance (MR) images. A novel skip connection U-net (SC U-net) was proposed and compared with the classical U-net. Experiments were performed on a dataset of 60 images, acquired from three different scanners. Validation analysis and cross-scanner testing were conducted. Compared with U-net, the proposed SC U-net had a faster convergence and higher segmentation accuracy. The software environment and models of the proposed system were made publicly accessible at Dockerhub.

Purpose: To compare sunitinib vs sorafenib plus transarterial chemoembolization (TACE) for inoperable hepatocellular carcinoma (HCC) patients., Methods: From January 2010 to December 2016, 104 patients with inoperable stage III HCC were included and randomly divided into two groups. Patients in the sunitinib+TACE (SU+TACE) group received sunitinib orally 37.5 mg daily, while patients in the sorafenib+TACE (SO+TACE) group received sorafenib 400 mg twice daily. The two groups were given sunitinib or sorafenib on an interrupted schedule, with a 4-7 days interval before or after TACE sessions. TACE treatment was repeated every 6-8 weeks. Patients were treated for about 4-6 cycles until the occurrence of toxicity or patient refusal, or progressive disease., Results: The median overall survival (OS) and the median progression-free survival (PFS) in the SO+TACE group were significantly higher than that in the SU+TACE (OS: p=0.017; PFS: p=0.036, respectively). The rates of response and disease control were higher in the SO+TACE group (58%, 79%, respectively) compared to the SU+TACE group (37%, 66%, respectively), although without statistical significance. Regarding the toxicities, we found higher rates of hand-foot skin reaction (HFSR) in the SO+TACE group, while frequent occurrence of thrombocytopenia and neutropenia in the SU+TACE group., Conclusions: The SO+TACE regimen was more effective and well tolerated in patients with unresectable stage III HCC compared to the SU+TACE regimen. The SO+TACE regimen may be a better alternative to the current standard regimens.

A mathematical description of the absolute surface height distribution in generalized fringe projection profilometry under large measuring depth range is presented. Based on least-squares polynomial fitting, a nonlinear calibration to determine the mapping between phase change and surface height is proposed by considering the unequal height arrangement of the projector and the camera. To solve surface height from phase change, an iteration method is brought forward. Experiments are implemented to demonstrate the validity of the proposed calibration and an accuracy of 0.3 mm for surface profile under 300 mm measuring depth can be achieved.

In fringe projection, the CCD camera and the projector are often placed at equal height. In this paper, we will study the calibration of an unequal arrangement of the CCD camera and the projector. The principle of fringe projection with two-dimensional digital image correlation to acquire the profile of object surface is described in detail. By formula derivation and experiment, the linear relationship between the out-of-plane calibration coefficient and the y coordinate is clearly found. To acquire the three-dimensional (3D) information of an object correctly, this paper presents an effective calibration method with linear least-squares fitting, which is very simple in principle and calibration. Experiments are implemented to validate the availability and reliability of the calibration method., (© 2011 Optical Society of America)

A novel shape based segmentation approach is proposed by modifying the external energy component of a deformable model. The proposed external energy component depends not only on the gray level of the images but also on the shape information which is obtained from the signed distance maps of objects in a given data set. The gray level distribution and the signed distance map of the points inside and outside the object of interest are accurately estimated by modelling the empirical density function with a linear combination of discrete Gaussians (LCDG) with positive and negative components. Experimental results on the segmentation of the kidneys from low-contrast DCE-MRI and on the segmentation of the ventricles from brain MRI's show how the approach is accurate in segmenting 2-D and 3-D data sets. The 2D results for the kidney segmentation have been validated by a radiologist and the 3D results of the ventricle segmentation have been validated with a geometrical phantom.

In this work, we aim at validating some soft tissue deformation models using high resolution Micro Computed Tomography (Micro-CT) and medium resolution Cone-Beam CT (CBCT) images. These imaging techniques play a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) even for small force loads, and provide good capabilities for creating accurate 3D models of tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite element (F.E.) models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remains a challenge. For our validation, ex vivo lamb liver is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We found that the LEM is more applicable to lamb liver than the LVEM for small force loads (< 40 g) and that the NHM is closer to reality than the LVEM for this same range of force loads.