Centroid Distance Research Articles

C–H⋯S Hydrogen Bond Assisted Supramolecular Encapsulation of Fullerenes with Nanobelts

C–H⋯S Hydrogen Bond Assisted Supramolecular Encapsulation of Fullerenes with Nanobelts

MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.

Dose escalation to dominant intraprostatic lesions (DILs) is a novel treatment strategy to improve the treatment outcome of prostate radiation therapy. Treatment planning requires accurate and fast delineation of the prostate and DILs. In this study, a 3D cascaded scoring convolutional neural network is proposed to automatically segment the prostate and DILs from MRI. The proposed cascaded scoring convolutional neural network performs end-to-end segmentation by locating a region-of-interest (ROI), identifying the object within the ROI, and defining the target. A scoring strategy, which is learned to judge the segmentation quality of DIL, is integrated into cascaded convolutional neural network to solve the challenge of segmenting the irregular shapes of the DIL. To evaluate the proposed method, 77 patients who underwent MRI and PET/CT were retrospectively investigated. The prostate and DIL ground truth contours were delineated by experienced radiologists. The proposed method was evaluated with fivefold cross-validation and holdout testing. The average centroid distance, volume difference, and Dice similarity coefficient (DSC) value for prostate/DIL are 4.3±7.5/3.73±3.78mm, 4.5±7.9/0.41±0.59cc, and 89.6±8.9/84.3±11.9%, respectively. Comparable results were obtained in the holdout test. Similar or superior segmentation outcomes were seen when compared the results of the proposed method to those of competing segmentation approaches. The proposed automatic segmentation method can accurately and simultaneously segment both the prostate and DILs. The intended future use for this algorithm is focal boost prostate radiation therapy.

Using the Improved SSD Algorithm to Motion Target Detection and Tracking.

Computer vision-based motion target detection and tracking, which is widely used in video surveillance, human-computer interaction, range interpretation, and other fields, is one of the current research hotspots in the field of computer vision. In engineering scenarios, the two are inseparable and need to work together to accomplish specific tasks. The related research is progressing rapidly, but there is still room for improving its timeliness, accuracy, and automation. In this paper, we summarize and classify some classical target detection methods, analyze the basic principles of convolutional neural networks, and analyze the classical detection algorithms based on region suggestion and deep regression networks. After that, we improve the SSD algorithm for the shortage of low-level feature convolution layers, which has insufficient feature extraction and leads to poor detection of small targets. For the motion target tracking problem, this paper studies the motion target tracking method based on support vector machine and proposes the tracking method of support vector regression and the corresponding online support vector regression solution method based on the analysis of support vector tracking method and structural support vector tracking method. In this paper, we propose a tracking method that fuses structural support vector machines and correlation filtering. The structure is based on the idea of Inception, which adds and replaces some feature convolution layers of the original network while maintaining the original lightweight backbone. The final experiments on the VOC data set demonstrate that the improved algorithm improves the average detection accuracy by 2.6% compared to the original algorithm and basically maintains the real-time speed as well. Experimental simulations on a subset of VOC data (human set) show a significant improvement in AP values and more effective and reliable detection tracking of moving targets. The stability and accuracy of motion target detection and tracking are improved by setting parameters, such as confidence level; the effectiveness and continuity of detection and tracking are judged by setting the interframe centroid distance.

Investigation on the bouncing and coalescence behaviors of bubble pairs based on an improved APR-SPH method

Bubbles rising in fluids are widespread in many engineering fields. In this paper, the bouncing and coalescing of two bubbles rising side by side are studied based on a multi-phase smoothed particle hydrodynamics (SPH) model. An improved adaptive particle refinement (APR) scheme is applied in the SPH model to make the numerical simulation sufficiently accurate and efficient. The effect of the surface-tension model is firstly verified, then various testing cases including the rising of a single bubble and two horizontally positioned bubbles are carried out and validate the numerical model well. Furthermore, the interaction between two three-dimensional bubbles rising side by side is studied considering the effects of viscous forces (with the Reynolds number Re), surface tensions (with the Eotvos number Eo), and the initial centroid distance of the two bubbles (Γ). The motion and the deformation of the bubbles are displayed and the mechanism of the bubbles' interaction is discussed. The results reveal that for the lower Eotvos number cases indicating a larger surface tension effect, the surface tension force increases dramatically when the two bubbles collide with each other and coalesce.

Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.

Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7mm and 9.7mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervention (p = 0.81). Finally, three radiologists assess both the original and the range-reduced images for evaluating the effect of the range reduction method on their clinical decisions. We conclude that the automatic liver scan range generation method is able to reduce excess radiation compared to the manual performance with a high accuracy and without penalizing the clinical decision.

Tumor deformation correction for an image guidance system in breast conserving surgery.

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

Powder X-ray diffraction as a powerful tool to exploit in organic electronics: shedding light on the first N,N',N''-trialkyldiindolocarbazole.

The first crystal structure of a fully N-alkylated diindolocarbazole derivative, namely, 5,8,14-tributyldiindolo[3,2-b;2',3'-h]carbazole (1, C36H39N3), has been determined from laboratory powder X-ray diffraction (PXRD) data. A complex trigonal structure with a high-volume unit cell of 12987 Å3 was found, with a very long a(=b) [52.8790 (14) Å] and a very short c [5.36308 (13) Å] unit-cell parameter (hexagonal setting). The detailed analysis of the intermolecular interactions observed in the crystal structure of 1 highlights its potential towards the implementation of this core as a semiconductor in organic thin-film transistor (OTFT) devices. Since the molecule has a flat configuration reflecting its π-conjugated system, neighbouring molecules are found to stack atop each other in a slipped parallel fashion via π-π stacking interactions between planes of ca 3.30 Å, with a centroid-centroid distance between the aromatic rings corresponding to the shortest axis of the unit cell (i.e. c). The alkylation of the three N atoms proves to be a decisive feature since it favours the presence of C-H...π interactions in all directions, which strengthens the crystal packing. As a whole, PXRD proves to be a valuable option for the resolution of otherwise inaccessible organic crystal structures of interest in different areas.

Effects of π‐conjugation on the charge‐transport properties of hole‐transporting materials featuring diphenylamine‐π‐quinacridone for perovskite solar cells: A theoretical study

AbstractDensity functional theory (DFT), time‐dependent DFT, and Marcus theory were used to probe the optoelectronic and charge‐transport properties of compounds obtained by inserting long‐chain aliphatic alkenes or condensed aromatic rings between the planar quinacridone core and the terminal donor diphenylamine moiety of a reference hole‐transporting material (HTM). Compared to the reference HTM, its newly designed derivatives showed lower‐lying highest occupied molecular orbitals that were well matched in energy with the valence band maximum of a representative perovskite absorber. HTMs obtained via the insertion of condensed aromatic rings showed higher hole mobilities than those obtained via the insertion of aliphatic alkenes. Overall, hole mobility was mainly influenced by the charge‐transfer integral, while other factors, such as the hole reorganization energy, hole hopping rate, and centroid distance, had only minor effects.

Ferrocene-Containing Pseudorotaxanes in Crystals: Aromatic Interactions with Hammett Correlation.

Single crystals of pseudorotaxanes, [(FcCH2NH2CH2Ar)(DB24C8)][PF6] (DB24C8 = dibenzo[24]crown-8, Fc = Fe(C5H4)(C5H5), Ar = -C6H3-3,4-Cl2, -C6H3-3,4-F2, -C6H4-4-F, -C6H4-4-Cl, -C6H4-4-Br, -C6H3-3-F-4-Me, -C6H4-4-I) and [(FcCH2NH2CH2C6H4-4-Me)(DB24C8)][Ni(dmit)2] (dmit = 1,3-dithiole-2,4,5-dithiolate), were obtained from solutions containing DB24C8 and ferrocenylmethyl(arylmethyl)ammonium. X-ray crystallographic analyses of the pseudorotaxanes revealed that the aryl ring of the axle moiety and the catechol ring of the macrocyclic component were at close centroid distances and parallel or tilted orientation. The structures with parallel aromatic rings showed correlation of the distances between the centroids to Hammett substituent constants of the aryl groups.

A robotic magnetic resonance-guided high-intensity focused ultrasound platform for neonatal neurosurgery: Assessment of targeting accuracy and precision in a brain phantom.

Intraventricular hemorrhage (IVH) is one of the most serious neurovascular complications resulting from premature birth. It can result in clotting of blood within the ventricles, which causes a buildup of cerebrospinal fluid that can lead to posthemorrhagic ventricular dilation and posthemorrhagic hydrocephalus. Currently, there are no direct treatments for these blood clots as the standard of care is invasive surgery to insert a shunt. Magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) has been investigated as a noninvasive treatment to lyse blood clots. However, current MRgHIFU systems are not suitable in the context of treating IVH in neonates. We have developed a robotic MRgHIFU neurosurgical platform designed to treat the neonatal brain. This platform facilitates ergonomic patient positioning and directs treatment through their open anterior fontanelle while providing a larger treatment volume. The platform is based on an MR-compatible robot developed by our group. Further development of the platform has warranted investigation of its targeting ability to assess its feasibility in the neonatal brain. This study aimed to quantify the platform's targeting accuracy, precision, and repeatability using a brain phantom and clinical MRI system. A thermosensitive brain-mimicking phantom was developed to test the platform's targeting accuracy. Rectangular grid patterns were created with HIFU thermal energy "lesions" in the phantoms by targeting specific coordinate points. The intended target locations were demarcated by inserting carbon fiber rods through a targeting assessment template. Coordinates for the intended and actual targets were derived from T2-weighted MRI scans, and the centroid distance between them was measured. Subsequently, the platform's targeting accuracy was quantified according to equations derived from ISO Standard 9283:1998. HIFU ablation resulted in distinct thermal lesions within the thermosensitive phantoms, which appeared as discrete hypointense regions in T2-weighted MR scans. A total of 127 target points were included in the data analysis, which yielded a targeting accuracy of 0.6mm and targeting precision of 1.2mm. The robotic MRgHIFU platform was shown to have a high degree of accuracy, precision, and repeatability. The results demonstrate the platform's functionality when targeting through simulated brain matter. These results serve as an initial verification of the platform targeting ability and showed promise toward the final application in a neonatal brain.

Design and implementation of shape-based feature extraction engine for vision systems using Zynq SoC

With the great impact of vision and Artificial Intelligence (AI) technology in the fields of quality control, robotic assembly and robot navigation, the hardware implementation of object detection and classification algorithms on embedded platforms has got ever-increasing attention these days. The real-time performance with optimum resource utilization of the implementation and its reliability as well as the robustness of the underlying algorithm is the overarching challenges in this field. In this work, an approach employing a fast and accurate vision-based shape-detection algorithm has been proposed and its implementation in heterogeneous System on Chip (SoC) is discussed. The proposed system determines centroid distance and its Fourier Transform for the object feature vector extraction and is realized in the Zybo Z7 development board. The ARM processor is responsible for communication with the external systems as well as for writing data to the Block RAM (BRAM), the control signals for efficient execution of the memory operations are designed and implemented using Finite State Machine (FSM) in the Programmable Logic (PL) fabric. Shape feature vector determination has been accelerated using custom modules developed in Verilog, taking full advantage of the possible parallelization and pipeline stages. Meanwhile, industry-standard Advanced Extendable Interface (AXI) buses are adopted for encapsulating standardized IP cores and building high-speed data exchange bridges between units within Zynq-7000. The developed system processes images of size 32 × 64 in real-time and can generate feature descriptors at a clock rate of 62MHz. Moreover, the method yields a shape feature vector that is computationally light, scalable and rotation invariant. The hardware design is validated using MATLAB for comparative studies

BN-Substitution in Dithienylpyrenes Prevents Excimer Formation in Solution and in the Solid State.

Boron–nitrogen substitutions in polycyclic aromatic hydrocarbons (PAHs) have a strong impact on the optical properties of the molecules due to a significantly more heterogeneous electron distribution. However, besides these single-molecule properties, the observed optical properties of PAHs critically depend on the degree of intermolecular interactions such as π–π-stacking, dipolar interactions, or the formation of dimers in the excited state. Pyrene is the most prominent example showing the latter as it exhibits a broadened and strongly bathochromically shifted emission band at high concentrations in solution compared to the respective monomers. In the solid state, the impact of intermolecular interactions is even higher as it determines the crystal packing crucially. In this work, a thiophene-flanked BN-pyrene (BNP) was synthesized and compared with its all-carbon analogue (CCP) in solution and in the solid state by means of crystallography, NMR spectroscopy, UV–vis spectroscopy, and photoluminescence (PL) spectroscopy. In solution, PL spectroscopy revealed the solvent-dependent presence of excimers of CCP at high concentrations. In contrast, no excimers were found in BNP. Clear differences were also observed in the single-crystal packing motifs. While CCP revealed overlapped pyrene planes with centroid distances in the range of classical π-stacking interactions, the BNP scaffolds were displaced and significantly more spatially separated.

SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients.

The purpose of this study was to evaluate the prognostic value of novel geometric variables obtained from pre-treatment [18F]FDG PET/CT with respect to classical ones in patients with non-small cell lung cancer (NSCLC). Retrospective study including stage I-III NSCLC patients with baseline [18F]FDG PET/CT. Clinical, histopathologic, and metabolic parameters were obtained. After tumor segmentation, SUV and volume-based variables, global texture, sphericity, and two novel parameters, normalized SUVpeak to centroid distance (nSCD) and normalized SUVmax to perimeter distance (nSPD), were obtained. Early recurrence (ER) and short-term mortality (STM) were used as end points. Univariate logistic regression and multivariate logistic regression with respect to ER and STM were performed. A cohort of 173 patients was selected. ER was detected in 49/104 of patients with recurrent disease. Additionally, 100 patients died and 53 had STM. Age, pathologic lymphovascular invasion, lymph nodal infiltration, TNM stage, nSCD, and nSPD were associated with ER, although only age (aOR = 1.06, p = 0.002), pathologic lymphovascular invasion (aOR = 3.40, p = 0.022), and nSPD (aOR = 0.02, p = 0.018) were significant independent predictors of ER in multivariate analysis. Age, lymph nodal infiltration, TNM stage, nSCD, and nSPD were predictors of STM. Age (aOR = 1.05, p = 0.006), lymph nodal infiltration (aOR = 2.72, p = 0.005), and nSPD (aOR = 0.03, p = 0.022) were significantly associated with STM in multivariate analysis. Coefficient of variation (COV) and SUVmean/SUVmax ratio did not show significant predictive value with respect to ER or STM. The geometric variables, nSCD and nSPD, are robust biomarkers of the poorest outcome prediction of patients with NSCLC with respect to classical PET variables. • In NSCLC patients, it is crucial to find prognostic parameters sinceTNM system alone cannot explain the variation in lung cancer survival. • Age, lymphovascular invasion, lymph nodal infiltration, and metabolic geometrical parameters were useful as prognostic parameters. • The displacement grade of the highest point of metabolic activity towards the periphery assessed by geometric variables obtained from [18F]FDGPET/CT was a robust biomarker of the poorest outcome prediction of patients with NSCLC.

Timing and magnitude of climate-driven range shifts in transboundary fish stocks challenge their management.

Climate change is shifting the distribution of shared fish stocks between neighboring countries’ Exclusive Economic Zones (EEZs) and the high seas. The timescale of these transboundary shifts determines how climate change will affect international fisheries governance. Here, we explore this timescale by coupling a large ensemble simulation of an Earth system model under a high emission climate change scenario to a dynamic population model. We show that by 2030, 23% of transboundary stocks will have shifted and 78% of the world's EEZs will have experienced at least one shifting stock. By the end of this century, projections show a total of 45% of stocks shifting globally and 81% of EEZs waters with at least one shifting stock. The magnitude of such shifts is reflected in changes in catch proportion between EEZs sharing a transboundary stock. By 2030, global EEZs are projected to experience an average change of 59% in catch proportion of transboundary stocks. Many countries that are highly dependent on fisheries for livelihood and food security emerge as hotspots for transboundary shifts. These hotspots are characterized by early shifts in the distribution of an important number of transboundary stocks. Existing international fisheries agreements need to be assessed for their capacity to address the social–ecological implications of climate‐change‐driven transboundary shifts. Some of these agreements will need to be adjusted to limit potential conflict between the parties of interest. Meanwhile, new agreements will need to be anticipatory and consider these concerns and their associated uncertainties to be resilient to global change.

Ain’t Nothing like Family—Female Brown Bears Share Their Home Range with Relatives

Sociality in animal populations is a continuum, and interactions between conspecifics are meaningful for all vertebrates. Ignorance of social structures can lead to misunderstanding their ecology and, consequently, to unsuccessful species management. Here, we combined genetic and spatial data on radio-collared brown bears (Ursus arctos) to investigate kin-related home range overlap and kin-related centroid distance within central and eastern Finland. We found that the extent of home range overlap was positively correlated with relatedness among adult females. In addition, home range centroid distance decreased as relatedness increased. Moreover, there were significant differences between the two studied regions: female brown bears in central Finland were more closely related to each other, and the sizes of their home ranges were larger than those in eastern Finland. The smaller home ranges and lower degree of relatedness among bears in eastern Finland might be a result of the substantially higher hunting pressure in the area, combined with immigration of new unrelated individuals from Russia.

Effect and mechanism of doped graphene nanosheets on phase transition properties of sodium nitrate

Molten salt is regarded as one of most promising candidates for solar energy storage due to possessing stable properties and large energy storage densities. However, the intrinsically low thermal conductivity of molten salt has become a bottleneck for rapid heat storage and transport. The addition of nanoparticles is generally considered to be a most effective way to improve the thermal conductivity of molten salt phase change materials (PCMs), while the phase change enthalpies of the nanocomposite phase change materials usually show two opposite trends of enhancement or decrement. Furthermore, the reason for the abnormal change of phase change enthalpy has not been clear in the literature so far, so the mechanism of change needs to be further explored. In this work, graphene nanosheets (GNS)@NaNO<sub>3</sub> (sodium nitrate) nanocomposite phase change materials are prepared by the hydration ultrasonic method. The materials are characterized by scanning electron microscope, and the phase change characteristics are measured using differential scanning calorimeter. Molecular dynamics simulation is carried out to explain the mechanism for the formation of the NaNO<sub>3</sub> dense layer and the non-collateral decrease of the enthalpy from the microscopic level. With the increase of GNS mass fraction, the melting point of the GNS@NaNO<sub>3</sub> composite phase change material decreases slightly while the phase change enthalpy decreases significantly with a non-colligative trend. A 13.81% decrease of the theoretical phase change enthalpy is observed with a GNS doping ratio of 1.5%. The NaNO<sub>3</sub> clusters observed on the surface of GNS are considered to have not melted, thereby resulting in a reduction in the phase change enthalpy. The mechanism is further investigated by molecular dynamics simulation, showing that the strong van der Waals attraction between GNS and NaNO<sub>3</sub> leads the 2–4 Å-thick NaNO<sub>3</sub> dense layer to form in the vicinity of GNS. With the increase of GNS mass fraction, the centroid equivalent distance between the dense layer and GNS gradually increases, which leads their mutual attraction to first increase and then weaken. When GNS mass fraction is 1.5%, the centroid equivalent distance reaches the position closest to the potential well, leading to a strongest mutual attraction. In other words, the phase change enthalpy decreases most obviously at this mass fraction. Thus, some conclusions can be drawn as follows. The type of interaction between molten salt and nano-enhancers and the position of the potential well are the fundamental reasons for the thickness of molten salt dense layer and the reduction of phase change enthalpy. The calculation of the interaction energy can be used to guide the selection of the mass fraction of the nano-enhancers, so as to avoid the loss of core material cluster and phase change enthalpy caused by the introduction of the nano-enhancers to a greatest extent. The preparation cost of the composite phase change material can also be reduced to a certain extent.

Visual domain knowledge-based multimodal zoning for textual region localization in noisy historical document images

Document layout analysis, or zoning, is important for textual content analysis such as optical character recognition. Zoning document images such as digitized historical newspaper pages are challenging due to noise and quality of the document images. Recently, effective data-driven approaches, such as leveraging deep learning, have been proposed, albeit with the concern of requiring larger training data and thus incurring additional cost of ground truthing. We propose a zoning solution by incorporating a knowledge-driven document representation, gravity map, into a multimodal deep learning framework to reduce the amount of time and data required for training. We first generate a gravity map for each image, considering the centroid distance and area between a cell in a Voronoi tessellation and its content to encode visual domain knowledge of a zoning task. Second, we inject the gravity maps into a deep convolution neural network (DCNN) during training, as an additional modality to boost performance. We report on two investigations using two state-of-the-art DCNN architectures and three datasets: two sets of historical newspapers and a set of born-digital contemporary documents. Evaluations show that our solution achieved comparable segmentation accuracy using fewer training epochs and less training data compared to a naïve training scheme.

757 Cardio-TC role in patients selection for transcatheter edge to edge tricuspid repair

Abstract Aims Anatomic knowledge of the tricuspid valve (TV) is the first step in the management of patients with tricuspid regurgitation (TR) who are candidates for transcatheter tricuspid valve intervention (TTVI). Echocardiography is undoubtedly the first approach in assessing the aetiology and severity of TR and the size and function of the right chambers. Computed tomography (CT) provides a detailed morphological visualization of the cardiac structures owing to acquisition of 3D data with high spatial resolution. These findings may undoubtedly help in decision-making progress for novel transcatheter therapies. The purpose of the present study was to assess the geometrical changes of the TV complex using CT images, in patients suffering from functional TR and lead-induced TR. Methods The study population consisted of 21 consecutive patients with symptomatic severe TR referred to Policlinico Universitario Campus Biomedico between November 2020 and October 2021. Patients were prospectively included in the study only if they presented severe TR, diagnosed by echocardiography and underwent cardiac CT study dedicated to the right-chambers. The reconstructions were transferred to an external workstation for off-line image analysis. The following measurements were reported: tricuspid annulus area, perimeter, septal–lateral and antero-posterior diameters. Commissures were identified as antero-septal (AS), postero-septal (PS) and anteroposterior (AP). Were measured the inferior vena cava ostium to tricuspid valve centroid distance, anatomic regurgitant orifice area (AROA) and its position respect to the centroid, and the right chambers. Results All 21 patients underwent CT scan using Siemens SOMATOM Definition AS 128 Slice CT Machine. The measurements were calculated off-line using the 3mensio workstation. In our study population, the annulus resulted enlarged in the annulus area, perimeter, septal-lateral and anterior-posterior dimensions. Measurements did not differ significantly, except for the septal-lateral diameter that was smaller in systole (52.80 ± 7.28 mm vs. 47.83 ± 6.83 mm (P=0.027). Also, distances between the commissures were similar except for the AP-AS distance that was shorter in systole (45.26 ± 3.48 mm vs. 42.13 ± 3.73, P=0.007). The AROA resulted to be central in 7 patients, the IVC ostium to TV centroid distance was 23±3 mm. Right chambers and IVC resulted very enlarged in all patients. Conclusions CT provides a complete morphologic imaging of the heart structures, thanks to a high spatial resolution with excellent capacity to define the endocardial border and allows acquisition of three-dimensional data with high spatial resolution of the TV and provides valuable information about the geometric variations of the tricuspid complex in patients with TR. Image quality for analysis should be optimized with specific CT acquisition protocols that focus on the right ventricles.

Automatic coarse registration of point clouds using plane contour shape descriptor and topological graph voting

Registration is a basic yet crucial task in point cloud processing. Obtaining robust as well as accurate features and correspondences is critical to feature-based point cloud registration. Aiming at structured urban scenes, this paper employs plane as primitive for feature extraction and proposes an automatic coarse registration algorithm of pairwise point clouds based on plane contour shape descriptor and topological graph voting. It contains three major steps: First, in order to improve the efficiency and accuracy of plane extraction, an efficient voxel-based plane segmentation algorithm is applied, then a RANSAC plane fitting method is used to obtain high precision plane parameters. Second, we propose a plane contour shape descriptor, hole inner angle combined with triangular centroid distance (HIA-TCD), for obtaining corresponding planes in two laser scans, which is robust to the contour shape matching in the urban scene. Finally, according to the position relationship between plane correspondences, a topological graph is built, and an edge voting strategy is proposed to eliminate potential unreliable plane correspondences, then the optimal matched planes are selected to estimate the rigid transformation. The algorithm is tested by three sets of large-scale outdoor and two sets of indoor scene data, and the experimental results show that the presented algorithm can effectively register the pairwise point cloud. In the outdoor scene, the rotation errors are less than 0.3°, and the translation errors are less than 0.1 m. In the indoor scene, the rotation errors are less than 0.2°, and the translation errors are less than 0.1 m. In addition, our algorithm is superior to the state-of-the-art in efficiency.

Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.