Segmentation Of Structures Research Articles

Applicability of a new method for mesoscopic structure segmentation of asphalt mixture based on two-dimensional image

The mechanical properties of the asphalt mixture largely depend on its internal structure. Studies in mixture mesostructure are of great significance for perfecting relevant theoretical systems and improving mixture design methods. However, efficient segmentation of mesostructure is an important precondition for mesoscale research about asphalt mixtures. Therefore, this article proposes a new method for mesostructure information acquisition that mainly improves the function of image preprocessing and separation of touching objects. A visual and quantitative comparison between the proposed and common methods is adopted. The result shows that the proposed method performs better in identifying small particles and retaining the fidelity of the boundary. Besides, the mesostructure of four types of asphalt mixtures are compared based on the proposed method. The result shows that the optimum sample size, which makes the measured 2D information efficiently close to the actual 3D information, is around 20–30. Compared to SUP25, the coordination number of AC20 is about 0.15–0.23 higher. Compared with the samples compacted by Marshall, the samples compacted by gyratory have a larger coordination number, about 0.12–0.21 higher, a larger fractal dimension, about 0.059–0.062 higher, a smaller uniformity of major axis orientation, about 1.53–1.28 lower, and a smaller uniformity of area distribution, about 0.063–0.12 lower.

Strategy for Forming Neighborhood-Based Business Entities as A Motor for Community Economic Potential in Samarinda City, Indonesia

Government administration becomes a key success in developing the local potential sector. Mapping local potential, in this case, sub-districts is one of the important things to do to find and recognize economic potential that can be developed. Ideally, community economic activities at the sub-district level are carried out as institutions or business entities managed professionally but still rely on the area's potential. It can make community businesses more productive and effective. Thus, this study seeks to determine the potential for forming sub-district-based business entities as one of the economic drivers of the city of Samarinda. This study uses a qualitative approach to primary and secondary data. Also, the research location is 59 sub-districts in the city of Samarinda. A total of 59 village heads who were the target of this study, 55 people were willing to answer the questions. The data was analyzed using tabulating interviews through 9 blocks covering the Business Model Canvas components: Customer Segment, Value Proposition, Channel, Customer Relation, Revenue Stream, Key Resources, Key Activities, Key Partnerships, and Cost Structure. Also, Business Model Canvas and SWOT analysis. The result indicates that 10 sub-district-based business models have the most potential to be developed in Samarinda City, namely trading businesses (groceries, restaurants), home industry businesses (weaving, beads, amplang, fish floss, herbal medicine), service businesses (workshops, transportation services, Savings and Loans, motorbike washing, haircuts, sewing), Use of land that is not (RTH), Service-based environmental management system (such as environmental safety and environmental cleanliness), Become a wholesaler, for goods/needs that are for public consumption. Homestead agricultural food sector business (mushroom business, etc.), freshwater fish management, waste recycling, and tourism.

Ocular biometrics in eyes with different white-to-white corneal diameter in young myopic adults.

The interactions between white-to-white corneal diameter (WTW) and other ocular biometrics are important for planning of refractive surgery and understanding of ocular structural changes in myopia, but such interactions are rarely investigated in young myopic adults. This is a retrospective study involving 7893 young myopic adults from five centers. WTW and other ocular biometrics were measured by Pentacam. The ocular biometrics included anterior corneal curvature (AK) and posterior corneal curvature (PK), central corneal thickness (CCT) and corneal volume (CV), anterior and corneal eccentricity and asphericity, anterior corneal astigmatism (ACA) and posterior corneal astigmatism, anterior chamber depth (ACD), and anterior chamber volume (ACV). The ocular biometrics were compared among eyes of different WTW quartiles. Multivariate linear regression was used to assess the linear associations between WTW and other ocular biometrics adjusting for age, gender and spherical equivalent. In eyes of different WTW quartiles, other ocular biometrics were also significantly different (all P < 0.05). After adjusting for age, gender and spherical equivalent, WTW was positively correlated to AK (β = 0.26 to 0.29), ACA (β = 0.13), anterior corneal asphericity (β = 0.05), PK (β = 0.33 to 0.34), posterior corneal asphericity (β = 0.13), ACD (β = 0.29), and ACV (β = 40.69), and was negatively correlated to CCT (β = - 6.83), CV (β = - 0.06 to - 0.78), anterior corneal eccentricity (β = - 0.035), and posterior corneal eccentricity (β = - 0.14) (all P < 0.001). In conclusion, we found that in young myopic adults, larger WTW was associated with thinner corneal thickness, flatter corneal curvature, more anterior corneal toricity, less corneal eccentricity and asphericity, and broader anterior chamber. Our findings may fill in the gap of literature, and help us better understand how the anterior segment structures interact with the WTW in myopia.

Research on 3D Measurement and Rapid Location Technique for Assembling the Special-Shaped Structural Segments

The pylon of the Xinshougang Bridge, a welded steel box arch with a variable cross-section, exhibits asymmetrical design and unevenly inclined limb ends. The segmented assembly of this uniquely shaped steel pylon necessitates precise splicing. In this paper, a novel three-point method for segment attitude positioning is proposed based on the local coordinate system. This method facilitates accurate coordinate translation and attitude alignment between the section preparation facility and the construction site. Furthermore, a predictive methodology for segment posture during erection is introduced, aiming to minimize misalignments at the connection interfaces. This approach accounts for potential deviations arising in manufacturing and erection phases. The synergistic application of these methods effectively ensures the precise geometric shape of the bending and twisting steel tower throughout its phased construction.

OAM-mode coupling by segmented helical-ring-core waveguides inscribed with a femtosecond laser beam.

Azimuthally segmented helical-ring-core waveguides (HRCWs) were fabricated in silica glass by direct laser writing. Triple-segmented waveguides provided single-mode or few-mode light guidance depending on the core diameter. Bragg resonance reflection of the light carrying orbital angular momentum (OAM) was registered confirming the generalized angular phase-matching condition, which considers the azimuthally segmented structure of the waveguides.

VWD domain stabilization by autocatalytic Asp-Pro cleavage.

Domains known as von Willebrand factor type D (VWD) are found in extracellular and cell-surface proteins including von Willebrand factor, mucins, and various signaling molecules and receptors. Many VWD domains have a glycine-aspartate-proline-histidine (GDPH) amino-acid sequence motif, which is hydrolytically cleaved post-translationally between the aspartate (Asp) and proline (Pro). The Fc IgG binding protein (FCGBP), found in intestinal mucus secretions and other extracellular environments, contains 13 VWD domains, 11 of which have a GDPH cleavage site. In this study, we investigated the structural and biophysical consequences of Asp-Pro peptide cleavage in a representative FCGBP VWD domain. We found that endogenous Asp-Pro cleavage increases the resistance of the domain to exogenous proteolytic degradation. Tertiary structural interactions made by the newly generated chain termini, as revealed by a crystal structure of an FCGBP segment containing the VWD domain, may explain this observation. Notably, the Gly-Asp peptide bond, upstream of the cleavage site, assumed the cis configuration in the structure. In addition to these local features of the cleavage site, a global organizational difference was seen when comparing the FCGBP segment structure with the numerous other structures containing the same set of domains. Together, these data illuminate the outcome of GDPH cleavage and demonstrate the plasticity of proteins with VWD domains, which may contribute to their evolution for function in a dynamic extracellular environment.

Morphological Characterization and Assessment of Genetic Variability, Character Association using MOORA Method

Morphological Characterization. Electron microscopy, optical Microscopy, scanning probe microscopic like microscopy (SPM). Morphology using techniques characterizations were carried out. Morphology of nonmaterial’s various used for study A comprehensive of microscopic techniques The explanation is given here.The shape of animals and plants and of biology dealing with structure A division the form of an organism and organization or its parts. Amphibian morphology. The internal structure of words and Segmentation into different types of morphemes Two basic objectives or Essential to Morphology: New Words Creating and. Already Change of words in. Therefore, morphology is body Physically recognizable letterforms Read, they are morphological characters are referred to as of an organism Specific elements are shape, size, color And so on. Morphology is structure A word that describes a characteristic. activity, Taxonomy, heredity, ecology, development and other branches of biology A basis for understanding Morphology gives us, therefore other A for us to read the branches Provides the site. Very simply Said morphological classification, Physiology to establish species boundaries Attributes (or roles) Uses similarity, same As well as interracial relationships uses. Characteristics may be characteristic. The multi-objective optimization by ratio analysis (MOORA) method is one of the MADM techniques. It is a group of qualities (prospective students). It is possible to calculate the worth of criteria, making this the ideal choice for decision-makers like prospective students. Days to flowering, Days to maturity, Plant height, Branches per plant, Pods per plant, Seeds per pod. Genotypic variance, Phenotypic variance, Grand mean, Heritability. From the result it is seen that Plant height is got the first rank where as is the Days to flowering is having the lowest rank. As a result, the plant height received the first rank, as well as the days to flowering received the lowest rank.

Harmonic amide bond density as a game-changer for deciphering the crosslinking puzzle of polyamide

It is particularly essential to analyze the complex crosslinked networks within polyamide membranes and their correlation with separation efficiency for the insightful tailoring of desalination membranes. However, using the degree of network crosslinking as a descriptor yields abnormal analytical outcomes and limited correlation with desalination performance due to imperfections in segmentation and calculation methods. Herein, we introduce a more rational parameter, denoted as harmonic amide bond density (HABD), to unravel the relationship between the crosslinked networks of polyamide membranes and their desalination performance. HABD quantifies the number of distinct amide bonds per unit mass of polyamide, based on a comprehensive segmentation of polyamide structure and consistent computational protocols derived from X-ray photoelectron spectroscopy data. Compared to its counterpart, HABD overcomes the limitations and offers a more accurate depiction of the crosslinked networks. Empirical data validate that HABD exhibits the expected correlation with the salt rejection and water permeance of reverse osmosis and nanofiltration polyamide membranes. Notably, HABD is applicable for analyzing complex crosslinked polyamide networks formed by highly functional monomers. By offering a powerful toolbox for systematic analysis of crosslinked polyamide networks, HABD facilitates the development of permselective membranes with enhanced performance in desalination applications.

Diffusion model-based text-guided enhancement network for medical image segmentation

In recent years, denoising diffusion models have achieved remarkable success in generating pixel-level representations with semantic values for image generation modeling. In this study, we propose a novel end-to-end framework, called TGEDiff, focusing on medical image segmentation. TGEDiff fuses a textual attention mechanism with the diffusion model by introducing an additional auxiliary categorization task to guide the diffusion model with textual information to generate excellent pixel-level representations. To overcome the limitation of limited perceptual fields for independent feature encoders within the diffusion model, we introduce a multi-kernel excitation module to extend the model’s perceptual capability. Meanwhile, a guided feature enhancement module is introduced in Denoising-UNet to focus the model’s attention on important regions and attenuate the influence of noise and irrelevant background in medical images. We critically evaluated TGEDiff on three datasets (Kvasir-SEG, Kvasir-Sessile, and GLaS), and TGEDiff achieved significant improvements over the state-of-the-art approach on all three datasets, with F1 scores and mIoU improving by 0.88% and 1.09%, 3.21% and 3.43%, respectively, 1.29% and 2.34%. These data validate that TGEDiff has excellent performance in medical image segmentation. TGEDiff is expected to facilitate accurate diagnosis and treatment of medical diseases through more precise deconvolutional structural segmentation.

Shape-constrained deformable brain segmentation: Methods and quantitative validation

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

Efficient production of copolymerized PA6-based polymer fibers: Oligomer control and direct melt spinning

Synthesis of PA6-based polyester amides (PEAs) is the one of main way to expand the application of PA6. Due to the residual oligomers in the polymer matrix, PEAs are also difficult to achieve directly melt spinning similar to PA6. In this work, the molecular structure designable PEA synthesis method proposed in our previous work was further investigated to control the oligomer content control by end-amino shielding at open-loop stages and efficient de-volatilization at polycondensation stages. The effect of the molecular weight of PA6 segments on oligomer formation was also investigated. The results show that the oligomers, caprolactam, and cyclic dimer contents of PEAs are reduced to 2 wt%, 0.38–0.76 wt%, and 0.2–0.3 wt%, which is lower 75%, 85%, and 50% compared to industrially produced PA6, respectively. It indicates that the molecular weight of PA6 segments, in addition to affecting the physicochemical properties, can also thermodynamically enable regulation of the type of oligomer generated. By adjusting the chain segment structure and controlling the reaction conditions, all PEAs can be directly melting spinning (1200 m/min) and maintains good physical and chemical properties. At the same time, the substantial reduction in oligomers means that PEAs can completely eliminate post-processing such as hydrothermal extraction, fiber production time is shortened from more than 40 h (PA6) to less than 15 h and resource consumption is reduced by more than 30%. This oligomer control method is also applicable to functional PA6, which lays the foundation for efficient and sustainable industrial production of copolymerized PA6 functional fibers on a large scale.

Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan

BackgroundPeri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunction may manifest as morphological changes in these structures, however, automated quantification of these structures is not possible and rather characterization often requires exogenous contrast agents and manual delineation.MethodsWe propose a deep learning architecture to automatically delineate the peri-sinus space (e.g., PSD and intravenous AG structures) using two cascaded 3D fully convolutional neural networks applied to submillimeter 3D T2-weighted non-contrasted MRI images, which can be routinely acquired on all major MRI scanner vendors. The method was evaluated through comparison with gold-standard manual tracing from a neuroradiologist (n = 80; age range = 11–83 years) and subsequently applied in healthy participants (n = 1,872; age range = 5-100 years), using data from the Human Connectome Project, to provide exemplar metrics across the lifespan. Dice-Sørensen and a generalized linear model was used to assess PSD and AG changes across the human lifespan using quadratic restricted splines, incorporating age and sex as covariates.ResultsFindings demonstrate that the PSD and AG volumes can be segmented using T2-weighted MRI with a Dice-Sørensen coefficient and accuracy of 80.7 and 74.6, respectively. Across the lifespan, we observed that total PSD volume increases with age with a linear interaction of gender and age equal to 0.9 cm3 per year (p < 0.001). Similar trends were observed in the frontal and parietal, but not occipital, PSD. An increase in AG volume was observed in the third to sixth decades of life, with a linear effect of age equal to 0.64 mm3 per year (p < 0.001) for total AG volume and 0.54 mm3 (p < 0.001) for maximum AG volume.ConclusionsA tool that can be applied to quantify PSD and AG volumes from commonly acquired T2-weighted MRI scans is reported and exemplar volumetric ranges of these structures are provided, which should provide an exemplar for studies of neurofluid circulation dysfunction. Software and training data are made freely available online (https://github.com/hettk/spesis).

SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image

The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.

Variability in Manual Segmentation of Temporal Bone Structures in Cone Beam CT Images.

Manual segmentation of anatomical structures is the accepted "gold standard" for labeling structures in clinical images. However, the variability in manual segmentation of temporal bone structures in CBCT images of the temporal bone has not been systematically evaluated using multiple reviewers. Therefore, we evaluated the intravariability and intervariability of manual segmentation of inner ear structures in CBCT images of the temporal bone. Preoperative CBCTs scans of the inner ear were obtained from 10 patients who had undergone cochlear implant surgery. The cochlea, facial nerve, chorda tympani, mid-modiolar (MM) axis, and round window (RW) were manually segmented by five reviewers in two separate sessions that were at least 1 month apart. Interreviewer and intrareviewer variabilities were assessed using the Dice coefficient (DICE), volume similarity, mean Hausdorff Distance metrics, and visual review. Manual segmentation of the cochlea was the most consistent within and across reviewers with a mean DICE of 0.91 (SD = 0.02) and 0.89 (SD = 0.01) respectively, followed by the facial nerve with a mean DICE of 0.83 (SD = 0.02) and 0.80 (SD = 0.03), respectively. The chorda tympani had the greatest amount of reviewer variability due to its thin size, and the location of the centroid of the RW and the MM axis were also quite variable between and within reviewers. We observed significant variability in manual segmentation of some of the temporal bone structures across reviewers. This variability needs to be considered when interpreting the results in studies using one manual reviewer.

DBNet-SI: Dual branch network of shift window attention and inception structure for skin lesion segmentation

The U-shaped convolutional neural network (CNN) has attained remarkable achievements in the segmentation of skin lesion. However, given the inherent locality of convolution, this architecture cannot capture long-range pixel dependencies and multiscale global contextual information effectively. Moreover, repeated convolutions and downsampling operations can readily result in the omission of intricate local fine-grained details. In this paper, we proposed a U-shaped network (DBNet-SI) equipped with a dual-branch module that combines shift window attention and inception structures. First, we proposed a dual-branch module that combines shift window attention and inception structures (MSI) to better capture multiscale global contextual information and long-range pixel dependencies. Specifically, we have devised a cross-branch bidirectional interaction module within the MSI module to enable information complementarity between the two branches in the channel and spatial dimensions. Therefore, MSI is capable of extracting distinguishing and comprehensive features to accurately identify the skin lesion boundaries. Second, we have devised a progressive feature enhancement and information compensation module (PFEIC), which progressively compensates for fine-grained features through reconstructed skip connections and integrated global context attention modules. The results of the experiment show the superior segmentation performance of DBNet-SI compared with other deep learning models for skin lesion segmentation in the ISIC2017 and ISIC2018 datasets. Ablation studies demonstrate that our model can effectively extract rich multiscale global contextual information and compensate for the loss of local details.

STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.

The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in diagnosing and managing cardiovascular illnesses, given its 3D+Time (3D+T) sequence. The existing deep learning methods are constrained in their ability to 3D+T CMR segmentation, due to: (1) Limited motion perception. The complexity of heart beating renders the motion perception in 3D+T CMR, including the long-range and cross-slice motions. The existing methods' local perception and slice-fixed perception directly limit the performance of 3D+T CMR perception. (2) Lack of labels. Due to the expensive labeling cost of the 3D+T CMR sequence, the labels of 3D+T CMR only contain the end-diastolic and end-systolic frames. The incomplete labeling scheme causes inefficient supervision. Hence, we propose a novel spatio-temporal adaptation network with clinical prior embedding learning (STANet) to ensure efficient spatio-temporal perception and optimization on 3D+T CMR segmentation. (1) A spatio-temporal adaptive convolution (STAC) treats the 3D+T CMR sequence as a whole for perception. The long-distance motion correlation is embedded into the structural perception by learnable weight regularization to balance long-range motion perception. The structural similarity is measured by cross-attention to adaptively correlate the cross-slice motion. (2) A clinical prior embedding learning strategy (CPE) is proposed to optimize the partially labeled 3D+T CMR segmentation dynamically by embedding clinical priors into optimization. STANet achieves outstanding performance with Dice of 0.917 and 0.94 on two public datasets (ACDC and STACOM), which indicates STANet has the potential to be incorporated into computer-aided diagnosis tools for clinical application.

DAG-BLOCK: A Novel Architecture for Scaling Blockchain-Enabled Cryptocurrencies

With the rapid development of blockchain technology and industries, scalability has been widely realized as one of the primary and urgent concerns for the large-scale adoption of blockchain, especially for cryptocurrencies. In this respect, directed acyclic graph (DAG) proves to be an elegant solution to scaling blockchain but suffers from weak consistency and security issues. In this article, we designed a novel DAG-BLOCK architecture for blockchain-enabled cryptocurrency markets in order to improve the scalability. In our work, DAG is used to replace the Merkel-tree-based transaction structure within the block, and a novel design of open blocks is proposed to enable user nodes to participate in verifying the transactions in blockchain systems. On this basis, we designed a new segmented market structure, in which each miner serves only a group of users instead of all users, so as to reduce miners’ workload and thus scale transaction processing capabilities. Our work can help improve the scalability of cryptocurrencies via evolving the underlying blockchain systems to graph-based distributed ledgers and is expected to shed new light on designing blockchain-based decentralized markets.

Interbank Decisions and Margins of Stability: an Agent-Based Stock-Flow Consistent Approach

This study investigates the functioning of modern payment systems through the lens of banks' maturity mismatch practices, and it examines the effects of banks' refusal to roll over short-term interbank liabilities on financial stability. Within an agent-based stock-flow consistent framework, banks can engage in two segments of the interbank market that differ in maturity, overnight and term. We compare two interbank matching scenarios to assess how bank-specific maturity targets, dependent on the dictates of the Net Stable Funding Ratio, impact the dynamics of the interbank market and the effectiveness of conventional monetary policies. The findings reveal that maturity misalignment between deficit and surplus banks compromises the interbank market's efficiency and increases reliance on the central bank's standing facilities. Monetary policy interest-rate steering practices also become less effective. The study also uncovers a dual stability-based configuration in the banking sector, resembling the segmented European interbank structure. This paper suggests that heterogeneous maturity mismatches between surplus and deficit banks may result in asymmetric funding frictions that might precede credit- and sovereign-risk explanations of interbank tensions. Also, a combined examination of macroprudential tools and rollover-based interbank dynamics can enhance our understanding of how regulatory changes impact the stability of heterogeneous banking sectors.

Unlocking organizational change: a deep dive through a data triangulation in healthcare

PurposeThe outbreak of the COVID-19 pandemic has significantly impacted healthcare systems, presenting unforeseen challenges that necessitated the implementation of change management strategies to adapt to the new contextual conditions. This study aims to analyze organizational changes within the total hip replacement (THR) surgery pathway at multiple levels, including macro, meso and micro. It employs data triangulation from various sources to gauge the complexity of the change process and comprehend how multi-level decision-making influenced an unexpected shift.Design/methodology/approachA multicentric, single in-depth case study was conducted using a mixed-methods approach. Data sources included patient-reported outcome measures specific to the THR pathway and carefully structured in-depth interviews administered to managers and clinicians in two healthcare organizations serving the same population.FindingsDecisions made at the macro level resulted in an overall reduction in surgical activities. Organizational changes at the meso level led to a complete cessation or partial reorganization of activities. Micro-level actions for change and adaptation revealed diverse and fragmented change management strategies.Practical implicationsOrganizations with segmented structures may require a robust and structured department for coordinating change management responses to prevent the entire system from becoming stuck in the absorptive phase of change. However, it is important to recognize that absorptive solutions can serve as a starting point for genuine innovations in change management.Originality/valueThe utilization of data triangulation enables the authors to visualize how specific changes implemented in response to the pandemic have influenced the observed outcomes. From a managerial perspective, it provides insights into how future innovations could be introduced.

A quantitative evaluation of the deep learning model of segmentation and measurement of cervical spine MRI in healthy adults.

To evaluate the 3D U-Net model for automatic segmentation and measurement of cervical spine structures using magnetic resonance (MR) images of healthy adults. MR images of the cervical spine from 160 healthy adults were collected retrospectively. A previously constructed deep-learning model was used to automatically segment anatomical structures. Segmentation and localization results were checked by experienced radiologists. Pearson's correlation analyses were conducted to examine relationships between patient and image parameters. No measurement was significantly correlated with age or sex. The mean values of the areas of the subarachnoid space and spinal cord from the C2/3 (cervical spine 2-3) to C6/7 intervertebral disc levels were 102.85-358.12 mm2 and 53.71-110.32 mm2 , respectively. The ratios of the areas of the spinal cord to the subarachnoid space were 0.25-0.68. The transverse and anterior-posterior diameters of the subarachnoid space were 14.77-26.56mm and 7.38-17.58mm, respectively. The transverse and anterior-posterior diameters of the spinal cord were 9.11-16.02mm and 5.47-10.12mm, respectively. A deep learning model based on 3D U-Net automatically segmented and performed measurements on cervical spine MR images from healthy adults, paving the way for quantitative diagnosis models for spinal cord diseases.