Local Segmentation Research Articles

Automatic vessel attenuation measurement for quality control of contrast-enhanced CT: Validation on the portal vein.

Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CTexams. The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CTexaminations. Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast-enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21examinations. The best classifier was found to be a random forest, with a precision of 0.892 in the held-out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2HU. The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time-consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast-enhanced CTexaminations.

Global–local consistent semi-supervised segmentation of histopathological image with different perturbations

A histopathological image is a microscopic image applied to examine cellular and tissue structures and identify any abnormalities or disease processes. Histopathological image segmentation is a prerequisite step for analyzing histopathological images that can divide an image into meaningful regions or objects to accurately classify and analyze tissue structures, cellular regions, or particular histological entities. However, the existing deep learning based pathological image segmentation methods require huge annotation efforts from the pathologists, which is labor-intensive and time-consuming. In this scenario, it has become a hotspot to leverage abundantly available unlabeled data to help learn segmentation models given limited labeled data. In this paper, we propose a global–local consistent semi-supervised segmentation (GLCS) model that enforce the consistency of the segmentation results with weak and strong perturbations on unlabeled data. In GLCS, we firstly generate different weak perturbations for each unlabeled sample, and then add a regularization term to ensure the segmentation consistency among different weak perturbations. Next, different from the existing studies applying the regression methods to match the segmentation results among different perturbations, our methods are based on the generative adversarial learning that can keep the global structure consistency among unlabeled data with different strength of perturbations. Finally, we also add a patch-correlation based regularization term to preserve the local structure similarity among different perturbations images. We validate our GLCS on three datasets, i.e. Glas, Crag and MoNuSeg. The experimental results show that our method can achieve to the dice ratio of 90.35, 82.61 and 81.60 with 1:1 proportion of labeled data, which are significantly superior to the state-of-the-art semi-supervised histopathological image segmentation methods. Our code is public available at https://github.com/ISBELLAG/GLCS.

Investigation into flow-induced internal corrosion direct assessment in small-diameter dry gas fluctuating pipeline

Small-diameter undulating natural gas pipelines with low gas transmission capacity do not have internal inspection conditions to grasp the internal corrosion status of the pipeline. This study focuses on a small-diameter undulating dry natural gas gathering and transportation pipeline, the internal corrosion mechanism of the pipeline is studied through corrosion products, and the specific location of the internal corrosion sensitive pipe segment is determined based on multiphase flow simulation and PMDT(pipeline non-contact magnetic anomaly detection); the excavation inspection is ultimately used to evaluate the internal status of the pipeline. The results demonstrate that the internal corrosion mechanism of ZL (ZI Lai) pipeline is O2 and CO2 synergistic corrosion in which O2 plays a dominant role, while the increasing flow rate will aggravate corrosion; The maximum general corrosion rate and critical inclination along the pipeline are 0.0218 mm/a and 18.4°, respectively. Furthermore, twelve corrosion sensitive segments have been identified, including two II-level and six III-level magnetic anomaly segments. The internal corrosion defects are localized at the 3–9o’clock position of the pipeline, with a maximum general corrosion rate not exceeding 0.0228 mm/a. Furthermore, the pipeline failure pressure of 12.27 MPa surpasses the maximum allowable working pressure of 8.84 MPa, ensuring its continued operational safety. The corrosion protection measures of reducing the water content of natural gas, adding vapor corrosion inhibitor and regularly detecting magnetic anomaly pipeline are developed.

Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.

Visual fire detection using deep learning: A survey

Visual Fire Detection (VFD), through the rapid and accurate identification of smoke and flame in images and videos, is crucial for early fire warning and reducing fire hazards. In recent years, the introduction of deep learning has significantly advanced this field, especially in the automatic extraction of discriminative features necessary for VFD. This paper provides a comprehensive review of the latest technological advancements in fire detection using deep learning, offering a broad perspective. Initially, it details the publicly available benchmark datasets widely used in VFD research and the corresponding evaluation metrics, providing a basis for researchers to assess the performance of various algorithms. Subsequently, we propose a systematic categorization framework, dividing VFD tasks into three key directions: fire classification, fire localization, and fire segmentation. For these directions, we thoroughly review the innovative improvements in deep learning models tailored for image and video inputs and discusses how these advancements enhance the accuracy and efficiency of fire detection. Finally, we highlight the challenges in the field and explore future research directions, intending to inspire and guide both newcomers and seasoned researchers in this area.

Multi-scale network with attention mechanism for underwater image enhancement

Underwater images suffer from severe degradation due to the complicated environments. Though numerous approaches have been proposed, accurately correcting color bias meanwhile effectively enhancing contrast is still a difficult problem. In order to address this issue, a dedicated designed Multi-scale Network with Attention mechanism (MNA) is introduced in this work. Concretely, MNA contains four key characteristics: (a) setting more convolution layers in shallow flows, (b) letting connections from high-level to adjacent low-level stream progressively, (c) simplifying dual attention mechanism embedding it in conventional residual block, (d) exploiting channel attention module to fuse multi-scale information rather than conventional summation operation. Extensive experiments demonstrate that our MNA achieves better performance than some well-recognized technologies. Meanwhile, ablation study proves the effectiveness of each component in our MNA. In addition, extended applications demonstrate the improvement of our MNA in local feature points matching and image segmentation.

Instrument fracture in periodontal therapy: ethical disclosure and clinical management - a case report

Periodontal instrument fractures are rare events in dentistry, with limited literature available on their occurrence and management. This case report highlights an incident involving the fracture of a periodontal sickle scaler blade during manual instrumentation for the removal of calculus. The fracture occurred during instrumentation on the mesial surface of the maxillary right second molar, and the separated blade was subsequently pushed into the sulcus. A radiographic assessment was performed to verify the precise location of the fractured segment. Following confirmation, the broken blade was subsequently retrieved using curved artery forceps. The case report highlights factors contributing to instrument fractures, emphasizing the importance of instrument maintenance, sterilization cycles, and operator technique. Ethical considerations regarding patient disclosure, informed consent, and instrument retrieval methods are well discussed. This case underscores the importance of truthful communication, the proper use of instruments, equipment maintenance in dentistry, and the significance of ongoing professional development to enhance treatment safety, proficiency, and ethical standards in dental care.

Discriminative Segment Focus Network for Fine-grained Video Action Recognition

Fine-grained video action recognition aims at identifying minor and discriminative variations among fine categories of actions. While many recent action recognition methods have been proposed to better model spatio-temporal representations, how to model the interactions among discriminative atomic actions to effectively characterize inter-class and intra-class variations has been neglected, which is vital for understanding fine-grained actions. In this work, we devise a Discriminative Segment Focus Network (DSFNet) to mine the discriminability of segment correlations and localize discriminative action-relevant segments for fine-grained video action recognition. Firstly, we propose a hierarchic correlation reasoning (HCR) module which explicitly establishes correlations between different segments at multiple temporal scales and enhances each segment by exploiting the correlations with other segments. Secondly, a discriminative segment focus (DSF) module is devised to localize the most action-relevant segments from the enhanced representations of HCR by enforcing the consistency between the discriminability and the classification confidence of a given segment with a consistency constraint. Finally, these localized segment representations are combined with the global action representation of the whole video for boosting final recognition. Extensive experimental results on two fine-grained action recognition datasets, i.e., FineGym and Diving48, and two action recognition datasets, i.e., Kinetics400 and Something-Something, demonstrate the effectiveness of our approach compared with the state-of-the-art methods.

Re-resection Rates and Disease Recurrence in Crohn's Disease - A Population-Based Study Using Individual-Level Patient Data.

Despite advances in the medical treatment of Crohn's disease (CD), many patients will still need bowel resections and face the subsequent risk of recurrence and re-resection. We describe contemporary re-resection rates and identify disease-modifying factors and risk factors for re-resection. We conducted a retrospective, population-based, individual patient data cohort study covering 47.4% of the Danish population, including all CD patients who underwent a primary resection between 2010 and 2020. Among 631 primary resected patients, 24.5% underwent a second resection, and 5.3% a third. Re-resection rates after one, five, and 10 years were 12.6%, 22.4%, and 32.2%, respectively. Reasons for additional resections were mainly disease activity (57%) and stoma reversal (40%). Disease activity-driven re-resection rates after one, five, and 10 years were 3.6%, 10.1%, and 14.1%, respectively. Most stoma reversals occurred within one year (80%). The median time to recurrence was 11.0 months. Biologics started within one year of the first resection revealed protective effect against re-resection for stenotic and penetrating phenotypes. Prophylactic biologic therapy at primary ileocecal resection reduced disease recurrence and re-resection risk (HR 0.58, 95% CI (0.34-0.99), p=0.047). Risk factors for re-resection were location of resected bowel segments at the primary resection, disease location, disease behavior, smoking, and perianal disease. Re-resection rates, categorized by disease activity, are lower than those reported in other studies and are closely associated with disease phenotype and localization. Biological therapy may be disease-modifying for certain subgroups when initiated within one year of resection.

Actin polymerization and longitudinal actin fibers in axon initial segment plasticity.

The location of the axon initial segment (AIS) at the junction between the soma and axon of neurons makes it instrumental in maintaining neural polarity and as the site for action potential generation. The AIS is also capable of large-scale relocation in an activity-dependent manner. This represents a form of homeostatic plasticity in which neurons regulate their own excitability by changing the size and/or position of the AIS. While AIS plasticity is important for proper functionality of AIS-containing neurons, the cellular and molecular mechanisms of AIS plasticity are poorly understood. Here, we analyzed changes in the AIS actin cytoskeleton during AIS plasticity using 3D structured illumination microscopy (3D-SIM). We showed that the number of longitudinal actin fibers increased transiently 3 h after plasticity induction. We further showed that actin polymerization, especially formin mediated actin polymerization, is required for AIS plasticity and formation of longitudinal actin fibers. From the formin family of proteins, Daam1 localized to the ends of longitudinal actin fibers. These results indicate that active re-organization of the actin cytoskeleton is required for proper AIS plasticity.

Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms.

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. Various automated approaches have been proposed, many of which work well for high-contrast datasets where the features of interest can be easily detected and are clearly separated from one another. Our inner ear stereocilia cryo-electron tomographic datasets are characterized by a dense array of hexagonally packed actin filaments that are frequently cross-connected. These features make automated segmentation very challenging, further aggravated by the high-noise environment of cryo-electron tomograms and the high complexity of the densely packed features. Using prior knowledge about the actin bundle organization, we have placed layers of a highly simplified ball-and-stick actin model to first obtain a global fit to the density map, followed by regional and local adjustments of the model. We show that volumetric model building not only allows us to deal with the high complexity, but also provides precise measurements and statistics about the actin bundle. Volumetric models also serve as anchoring points for local segmentation, such as in the case of the actin-actin cross connectors. Volumetric model building, particularly when further augmented by computer-based automated fitting approaches, can be a powerful alternative when conventional automated segmentation approaches are not successful.

DEM-CFD Simulation Analysis of Heat Transfer Characteristics for Hydrogen Flow in Randomly Packed Beds

In this study, three randomly packed beds with varying tube-to-particle diameter ratios (D/d) are constructed using the discrete element method (DEM) and simulated via CFD under low pore Reynolds numbers (Rep < 100). An innovation of this research lies in the application of hydrogen in randomly packed beds, coupled with the consideration of its temperature-dependent thermal properties. The axial analysis of the heat transfer characteristics shows that PB−5 and PB−6 achieve thermal equilibrium 44% and 58% faster than PB−4, respectively, demonstrating enhanced heat transfer efficiency. However, at higher flow rates (0.8 m/s), the large-sized fluid channels in PB−6 severely impact the heat transfer efficiency, slightly reducing it compared to PB−5. Additionally, this study introduces a localized segmentation method for calculating the axial local Nusselt number, showing that the axial local Nusselt number (Nu) not only exhibits an inverse relationship with the axial porosity distribution, but also matches its amplitude fluctuations. The wall effect significantly impacts the flow and temperature distribution in the packed bed, causing notable velocity and temperature oscillations in the near-wall region. In the near-wall region, the average temperature is lower than in the core region, and the axial temperature profile exhibits more intense oscillations. These findings may provide insights into the use of hydrogen in randomly packed beds, which are vital for enhancing industrial applications such as hydrogen storage and utilization.

Gender differences in patterns of cartilage loss: Data from the Osteoarthritis Initiative

OBJECTIVEUnderstanding gender-specific differences in patterns of cartilage loss can improve our knowledge of the pathogenesis of knee osteoarthritis (KOA) development and progression and may inform clinical trials of treatments for KOA. The goal of our observational study was to examine gender differences in patterns of cartilage loss in the central weight-bearing regions of the femur. METHODSWe measured cartilage volume change in the indexed knee of 700 subjects with Kellgren-Lawrence (KL) 1, 2, or 3 from the OAI for four follow-up periods (BL to 24mo, BL to 48mo, BL to 72mo, and BL to 96mo) using the local area cartilage segmentation (LACS) method. Briefly, the LACS method uses robust coordinate systems fixed to anatomical landmarks to measure patterns of change in cartilage volume in sub-regions using responsiveness heat maps. RESULTSWe observed a statistically significant gender difference in cartilage change in the medial femur (MF), lateral femur (LF), and medial tibia (MT). The heat maps, showed loss was primarily in the posterior central weight-bearing portion of the LF and more general in the LT and MF. Similar patterns were observed for each of the four follow-up periods. CONCLUSIONSThe LACS method was capable of illustrating gender-specific differences in patterns of cartilage loss that may offer insight into the variation of gender differences in the natural history of KOA and may be useful in evaluating the benefit of interventions for KOA.

The influence of shear surface quality on the mechanical properties of long-span prestressed continuous rigid-frame bridge

PurposeThe long-span continuous rigid-frame bridges are commonly constructed by the section-by-section symmetrical balance suspension casting method. The deflection of these bridges is increasing over time. Wet joints are a typical construction feature of continuous rigid-frame bridges and will affect their integrity. To investigate the sensitivity of shear surface quality on the mechanical properties of long-span prestressed continuous rigid-frame bridges, a large serviced bridge is selected for analysis.Design/methodology/approachIts shear surface is examined and classified using the damage measuring method, and four levels are determined statistically based on the core sample integrity, cracking length and cracking depth. Based on the shear-friction theory of the shear surface, a 3D solid element-based finite element model of the selected bridge is established, taking into account factors such as damage location, damage number and damage of the shear surface. The simulated results on the stress distribution of the local segment, the shear surface opening and the beam deflection are extracted and analyzed.FindingsThe findings indicate that the main factors affecting the ultimate shear stress and shear strength of the shear surface are size, shear reinforcements, normal stress and friction performance of the shear surface. The connection strength of a single or a few shear surfaces decreases but with little effect on the local stress. Cracking and opening mainly occur at the 1/4 span. Compared with the rigid “Tie” connection, the mid-span deflection of the main span increases by 25.03% and the relative deflection of the section near the shear surface increases by 99.89%. However, when there are penetrating cracks and openings in the shear surface at the 1/2 span, compared with the 1/4 span position, the mid-span deflection of the main span and the relative deflection of the cross-section increase by 4.50%. The deflection of the main span increases with the failure of the shear surface.Originality/valueThese conclusions can guide the analysis of deflection development in long-span prestressed continuous rigid-frame bridges.

Novel deep learning methods for 3D flow field segmentation and classification

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods are mainly based on global vorticity information and focused on 2D circumstance. In this paper, based on flow field theory, criteria for flow field segmentation and classification in 3D space using local velocity information and the physical relationship between local vorticity and vortex wake are conducted, and then novel physical local information-embedded flow field segmentation and classification methods in 3D space using Multilayer Perceptron (MLP) are proposed. Our methods consist of three modules: a preprocessing module, a flow field segmentation module and a flow field classification module. The preprocessing module calculates input for succeeding modules based on the above criteria. The segmentation and classification modules process corresponding input to identify vortex structure, and further classify the type of vortex wakes in a 3D flow field. Experimental results show that our method can identify vortex area with an accuracy rate that reaches 90% while reducing time consumption by more than 90% compared with existing segmentation methods; our classification method reaches an accuracy rate of 99.7% while reducing time consumption by more than 90% compared with existing classification methods. Our proposed methods proved to achieve good performance both on accuracy and efficiency.

Digitalization era: Investigating the spatial interplay between cyber human activity and economy with a hierarchical framework

The development of Information and Communication Technology has shifted human activity from offline to online, and promoted digital economy. These changes challenge traditional methodologies relying on physical human activity as a micro-level reflection of the macro-level economy. To address this, a hierarchical framework is proposed to characterize cyber human activity, incorporating activity diversity, size, and preference. Then, Ordinary Least Squares and Geographically Weighted Regression models are used to examine the spatial interplay between cyber human activity and township-level economy. K-Medoids clustering is further applied to coefficients in cyber GWR model to reveal mechanisms influencing local economies. Taking diverse geospatial data in Jilin Province, China as an example, the result indicates that the significant extreme values of cyber human activity have divided Jilin Province into four subregions, closely aligning with local economic segmentation. Moreover, the local economy can be better reflected by cyber human activity rather than physical ones, especially in highly digitized regions. Furthermore, the regions with similar influence mechanisms cluster geographically. These clusters are categorized into three types, namely, dynamics, robust, and balanced, representing different mechanisms influencing the local economy. Practical and theoretical implications are discussed, including using cyber human activity in assessing the economy and implementing adaptable economic policies at township level.

Arterial Lesion Location and Outcomes of Intracranial Atherosclerotic Disease.

Intracranial atherosclerosis is a leading cause of stroke with a high recurrence rate despite treatment. Numerous factors are proposed to influence stroke recurrence due to intracranial atherosclerosis including lesion eccentricity, plaque characteristics, and computational fluid dynamic metrics, such as wall shear stress. An overlooked variable that intrinsically relates to intracranial atherosclerosis is the location of the arterial segment where the lesion occurs. Variations in cerebral blood flow, arterial anatomy, and flow dynamics are likely drivers of initial lesion development and thus likely to influence stroke recurrence. To date, treatment trials of intracranial atherosclerosis have not considered arterial segment lesion location as an independent variable, failing to account for variations in flow dynamics between each artery. There are limited available data on differences between arterial segments, confined to only post hoc analyses. In this review, we summarize available data on such differences between arterial segments. With the limited arterial segment data available, multiple differences in recurrence of stroke in territory of lesion were identified across trials.

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation

Background and objectiveThe effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions. MethodsIt is critical for a segmentation network to effectively extract 3D discriminative features to distinguish esophageal cancers from some visually closed adjacent esophageal tissues and organs. In this work, an efficient HRU-Net architecture (High-Resolution U-Net) was exploited for esophageal cancer and esophageal carcinoma segmentation in CT slices. Based on the idea of localization first and segmentation later, the HRU-Net locates the esophageal region before segmentation. In addition, an Resolution Fusion Module (RFM) was designed to integrate the information of adjacent resolution feature maps to obtain strong semantic information, as well as preserve the high-resolution features. ResultsCompared with the other five typical methods, the devised HRU-Net is capable of generating superior segmentation results. ConclusionsOur proposed HRU-NET improves the accuracy of segmentation for squamous esophageal cancer. Compared to other models, our model performs the best. The designed method may improve the efficiency of clinical diagnosis of esophageal squamous cell carcinoma lesions.

Segmentation of skin lesion using superpixel guided generative adversarial network with dual-stream patch-based discriminators

Accurate skin lesion segmentation in dermoscopic images is crucially important for the early diagnosis of skin cancer. Nevertheless, the existence of complex interfering factors, such as natural and artificial artifacts (e.g., hair and air bubbles), irregular appearance (e.g., varied shapes and low contrast), and significant differences in color shades cause the segmentation of skin lesion challenging. In this study, we propose a superpixel-guided generative adversarial network (GAN) with dual-stream patch-based discriminators for segmentation of the skin lesion. Specifically, in the designed GAN, a new multi-scale context extraction module (MCEM) is designed in the mask generator to enrich contextual information and capture boundary features of the lesion region, and thus accurately locating the object boundary of the lesion. Meantime, another branch of superpixel guided discriminator is designed and added into the discriminator module, which supplies so more compact and semantic information that enhances the discriminative ability. By using the dual-branch patch-based discriminators, the fine-grained discriminative power to local segmentation details is further enhanced and in turn making the generator produce more accurate segmentation masks. Comprehensive experiments show that our presented model achieves significant segmentation performance on the ISIC2016, ISIC2018, and HAM10000 skin lesion challenge datasets, and outperforms several promising deep convolutional neural networks.

Exploring consumer segments for online food provisioning services: Online supermarkets vs. alternative food networks

Recent years have witnessed a surge in online food provisioning services spanning from online mass retail to alternative food networks with digital sales platforms. Hence, enhancing our understanding of consumer preferences in the digital food space is vital, as digitalization holds significant potential to alter consumer habits within the food sector. To address this, we surveyed a representative sample of Norwegian consumers. We employed best-worst scaling to assess their preferences for eighteen characteristics derived from a variety of online food provisioning services. The characteristics covered business model elements such as key partners, resources, activities, distribution channels, and customer relations. Our findings identify two consumer segments. The first segment, labeled the Convenience segment, prioritizes efficient delivery and user-friendly interfaces, characteristics that resonate with online supermarket services. The second segment labeled the Local segment, prioritizes a wide range of locally and sustainably produced food in line with the ethos of alternative food networks. Notably, the latter segment, accounting for 48 % of the respondents, views several of the convenience characteristics derived from online mass retail as important. Taking the convenience preferences of the Local segment into account could broaden the appeal of alternative food networks and other sustainability-oriented niche providers to a wider range of consumers.