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Improved 3D image segmentation for X-ray tomographic data of biaxial warp-knitted composites

JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2025年第7-8期44卷 344-357页

作者： Zheng, Kehong Wu, Chenglie Chen, Hao Zhang, Xiyan Wang, Zhenyu Pan, Zhongxiang Qiu, Bingjing Wu, Zhenyu Zhejiang Sci Tech Univ Coll Mech Engn Xiasha Zhejiang Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China CenterSinohydro Bur 12 Co Ltd Hangzhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Coll Mat & Text Hangzhou Peoples R China Zhejiang Univ Ctr Hypergrav Expt & Interdisciplinary Res Hangzhou Peoples R China

Identifying different features and phases within the biaxial warp-knitted composites enables accurate characterization of the internal changes of the composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be accurately segmented and quantitatively analyzed is still a challenge, primarily due to the low attenuation of materials, especially the low contrast between the weft yarn and warp yarns. Here, we use deep learning approaches to identify the boundary of the section of the tow on the 2D image, extract individual warp or weft tows automatically, and this is followed by computational modeling of composite materials. The results show that the performance of ResUNet++ model is better than that of other models for the biaxial warp-knitted composite specimen segmentation tasks. The special improved dataset augmentation algorithm is also a positive effective way to enhance network performance.

关键词： X-ray computed tomography (XCT) images dataset augmentation ResUNet plus plus model deep learning-based segmentation image-based modeling

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Isogeometric multipatch surface fitting in tomographic images: Application to lattice structures

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 436卷

作者： Bichet, D. Passieux, J. C. Perie, J. N. Bouclier, R. Univ Toulouse Inst Math Toulouse IMT CNRS UPSINSAUMR5219 135 Ave Rangueil F-31077 Toulouse France Univ Toulouse Inst Clement Ader ICA CNRS INSAISAEMines AlbiUPS 3 Rue Caroline Aigle F-31400 Toulouse France Inst Univ France IUF Paris France

Additive manufacturing has enabled the production of cellular architected (lattice) structures known for their exceptional mechanical performances. However, the printed components often exhibit geometric defects on a scale close to that of lattice struts, leading to significant deviations in mechanical behavior when comparing simulations based on the as-designed (defect-free) geometry with experimental tests on the as-manufactured (imperfect) geometry. In this work, we develop a method to extract an analysis-suitable CAD-based geometry from 3D scan data. We start by building a multipatch B-spline surface model of the as-designed lattice boundary, and then deform it to match its as-manufactured counterpart observed in a volumetric image. To achieve this, key contributions include a data fitting metric based on the Virtual image Correlation approach, combined with an image learning component;the integration of the membrane strain energy of the surface for regularization;the enforcement of higher continuity between patches where appropriate;an automatic estimation of the pose of the CAD object in the image;and the computation of a distance indicator map between the aligned CAD model and the as-manufactured surface. These elements enable comprehensive, accurate, and efficient measurement of geometric defects in lattice structures. Validated through various experiments, including those on a BCC lattice structure, this method achieves sub-voxel accuracy. Ultimately, it provides a compact and explicit representation of the as-manufactured geometry, maintaining the same CAD-based discretization as the initial design, and thereby facilitating quantitative defect assessment.

关键词： image-based modeling Digital twin Isogeometric analysis Active contours Virtual image correlation Multipatch B-spline

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Procedure-based component and architecture modeling from a single image

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VISUAL COMPUTER 2016年第2期32卷 151-166页

作者： Hou, Fei Qin, Hong Qi, Yue Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore SUNY Stony Brook NYU Dept Comp Sci New York NY USA

This paper advocates a new component-aware framework to reconstruct 3D architecture from a single image. Different from existing work, our motivation is to obtain a complete set of semantically correct 3D architectural components, which enables part reusability towards rapid model reproduction and facilitates model variation. The core of our system is a novel algorithm to adaptively segment repeated curved stripes (e. g., roof tiles, building floors) into individual elements, based on which 3D dimensions as well as architectural components are derived from a single image. Specially for Chinese architectures, we further devise an interactive method to identify outer columns based on userspecified inner columns. Finally, 3D components are generated and shape rules are derived, from which the buildings and their variants are constructed. Our newcomponent-aware framework minimizes the use of data resource (i. e., one single image) and emphasizes component utility during rapid 3D architecture reproduction by advocating a component-aware approach.

关键词： Architectural modeling image-based modeling Procedural modeling

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A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第11期58卷 2835-2844页

作者： Lai, Changxin Chen, Yu Wang, Tianyao Liu, Jun Wang, Qian Du, Yiping Feng, Yuan Shanghai Jiao Tong Univ Inst Med Imaging Technol Sch Biomed Engn Shanghai 200030 Peoples R China Fudan Univ Peoples Hosp Shanghai 5 Dept Radiol 801 Heqing Rd Shanghai 200240 Peoples R China

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 mu m from a coarse FE model with a mesh size of 280 mu m. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.

关键词： image-based modeling Mouse model Controlled cortical impact Super resolution Neural network

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Full-hexahedral structured meshing for image-based computational vascular modeling

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MEDICAL ENGINEERING & PHYSICS 2011年第10期33卷 1318-1325页

作者： De Santis, Gianluca De Beule, Matthieu Van Canneyt, Koen Segers, Patrick Verdonck, Pascal Verhegghe, Benedict Univ Ghent BioMMeda IBiTech BE-9000 Ghent Belgium

image-based computational modeling offers a virtual access to spatially and temporally high resolution flow and structural mechanical data in vivo. Due to inter-subject morphological variability, mesh generation represents a critical step in modeling the patient-specific geometry and is usually performed using unstructured tetrahedral meshing algorithms. Although hexahedral structured meshes are known to provide higher accuracy and reduce the computational costs both for Finite Element Analysis and Computational Fluid Dynamics, their application in computational cardiovascular studies is challenging due to the complex 3D and branching topology of vascular territories. In this study, we propose a robust procedure for structured mesh generation, tailoring the mesh structure to the subject-specific vessel topology. The proposed methodology is based on centerline-based synthetic descriptors (i.e. centerlines, radii and centerlines' normals) which are used to solve the meshing problem following a bottom-up approach. First, topologically equivalent block-structures are placed inside and outside the lumen domain. Then, a projection operation is performed, returning a parametric volume mesh which fits the original triangulated model with sub-micrometric accuracy. Additionally, a three-layered arterial wall (resembling the intima, media and adventitia) is artificially generated, with the possibility of setting variable thickness (e.g. proximal-to-distal tapering) and material anisotropy (e.g. position-dependent collagen-fibers' orientation). This new meshing procedure, implemented using open-source software packages only, is demonstrated on two challenging human cases, being an aortic arch and an abdominal aortic aneurysm. High-quality meshes are generated in both cases, according to shape-quality metrics. By increasing the computation accuracy, the developed meshing tool has the potential to further add "confidence" to the use of computational methods in vascular applicatio

关键词： image-based modeling Hexahedral mesh Centerline Patient-specific Finite Element Analysis Computational Fluid Dynamics pyFormex

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image-based 3D modeling as a damage prioritization tool for historic buildings in post-disaster areas: The case of the 2020 Beirut blast

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JOURNAL OF CULTURAL HERITAGE 2023年 62卷 314-321页

作者： Kallas, Joe Napolitano, Rebecca Penn State Univ Dept Architectural Engn University Pk PA 16802 USA

On August 4th, 2020, a colossal blast at the port of the Lebanese capital, one of the largest in history not caused by a nuclear reaction, changed Beirut's historic neighborhoods within seconds from a busy, vibrant and living cultural hub into an urban fabric of shattered buildings, collapsed masonry walls and scratched pitched roofs. With not enough preparedness for such a calamitous event, the affected local community faced significant challenges as they attempted to recover from the damage done. Once a heritage site is lost, damaged or destroyed, the recovery and rehabilitation process is slow, if (at all) possible. In the wake of such events, there has been a growing interest to digitize historic sites and buildings to preserve them (digitally) against unforeseen threats such as natural or man-made disasters. However, little work has been done to explore the role of 3D documentation for post-blast inspection of masonry historic buildings. The objective of this work is to expose an efficient workflow for accelerating the assessment process of damaged historic buildings by the application of digital technology in post-disaster areas, through the case study on the Beirut Blast. The originality of this work resides in the fact that this was the first time ever that Beirut and its historic buildings were massively digitized. This paper outlines how and why 3D image-based modeling was implemented, emphasizing on the timely benefits for disaster recovery and improvement of decision making in a similar emergency case. The results of the documentation produced a fast yet accurate 3D models of the affected historic buildings in Beirut, despite the challenging structural properties of the post-blast built environment. Post-processing of the data provided cultural heritage experts and engineers with valuable documents for structural deficiencies, damages and accessibility, which facilitated the remote assessment of the buildings and accelerated the implementation of emergen

关键词： Beirut image-based modeling Photogrammetry Disaster recovery Cultural heritage Post -blast inspection

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image-based metric heritage modeling in the near-infrared spectrum

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HERITAGE SCIENCE 2020年第1期8卷 1-12页

作者： Adamopoulos, Efstathios Bovero, Alessandro Rinaudo, Fulvio Univ Torino Dept Comp Sci Corso Svizzera 185 I-10149 Turin TO Italy Fdn Ctr Conservaz & Restauro Beni Culturali La Ve Via 20 Settembre 18 I-10078 Venaria TO Italy Politecn Torino Dept Architecture & Design Viale Mattioli 39 I-10125 Turin Italy

Digital photogrammetry and spectral imaging are widely used in heritage sciences towards the comprehensive recording, understanding, and protection of historical artifacts and artworks. The availability of consumer-grade modified cameras for spectral acquisition, as an alternative to expensive multispectral sensors and multi-sensor apparatuses, along with semi-automatic software implementations of Structure-from-Motion (SfM) and Multiple-View-Stereo (MVS) algorithms, has made more feasible than ever the combination of those techniques. In the research presented here, the authors assess image-based modeling from near-infrared (NIR) imagery acquired with modified consumer-grade cameras, with applications on tangible heritage. Three-dimensional (3D) meshes, textured with the non-visible data, are produced and evaluated. Specifically, metric evaluations are conducted through extensive comparisons with models produced with image-based modeling from visible (VIS) imagery and with structured light scanning, to check the accuracy of results. Furthermore, the authors observe and discuss, how the implemented NIR modeling approach, affects the surface of the reconstructed models, and may counteract specific problems which arise from lighting conditions during VIS acquisition. The radiometric properties of the produced results are evaluated, in comparison to the respective results in the visible spectrum, on the capacity to enhance observation towards the characterization of the surface and under-surface state of preservation, and consequently, to support conservation interventions.

关键词： image-based modeling Photogrammetry Near-infrared imaging Modified cameras Cultural heritage survey

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modeling Fibrous Tissue in Vascular Fluid-Structure Interaction: A Morphology-based Pipeline and Biomechanical Significance

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2025年第1期41卷 e3892页

作者： Sun, Yujie Huang, Jiayi Lu, Qingshuang Yue, Xinhai Huang, Xuanming He, Wei Shi, Yun Liu, Ju Southern Univ Sci & Technol Dept Mech & Aerosp Engn Shenzhen Guangdong Peoples R China Fudan Univ Zhongshan Hosp Inst Vasc Surg Dept Vasc Surg Shanghai Peoples R China

modeling fibrous tissue for vascular fluid-structure interaction analysis poses significant challenges due to the lack of effective tools for preparing simulation data from medical images. This limitation hinders the physiologically realistic modeling of vasculature and its use in clinical settings. Leveraging an established lumen modeling strategy, we propose a comprehensive pipeline for generating thick-walled artery models. A specialized mesh generation procedure is developed to ensure mesh continuity across the lumen and wall interface. Exploiting the centerline information, a series of procedures are introduced for generating local basis vectors within the arterial wall. The procedures are tailored to handle thick-walled tissues where basis vectors may exhibit transmural variations. Additionally, we propose methods for accurately identifying the centerline in multi-branched vessels and bifurcating regions. These modeling approaches are algorithmically implementable, rendering them readily integrable into mainstream cardiovascular modeling software. The developed fiber generation method is evaluated against the strategy using linear elastostatics analysis, demonstrating that the proposed approach yields satisfactory fiber definitions in the considered benchmark. Finally, we examine the impact of anisotropic arterial wall models on the vascular fluid-structure interaction analysis through numerical examples, employing the neo-Hookean model for comparative purposes. The first case involves an idealized curved geometry, while the second studies an image-based abdominal aorta model. Our numerical results reveal that the deformation and stress distribution are critically related to the constitutive model of the wall, whereas hemodynamic factors are less sensitive to the wall model. This work paves the way for more accurate image-based vascular modeling and enhances the prediction of arterial behavior under physiologically realistic conditions.

关键词： anisotropic material model fluid-structure interaction image-based modeling patient-specific simulation vascular biomechanics

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INFLUENCE OF VESSEL ROUGHNESS ON WALL SHEAR STRESS IN image-based BLOOD FLOW modeling

INFLUENCE OF VESSEL ROUGHNESS ON WALL SHEAR STRESS IN IMAGE-...

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7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

作者： Xiong, Guanglei Taylor, Charles A. Stanford Univ Biomed Informat Program Stanford CA 94305 USA Stanford Univ Dept Bioengn & Surg Stanford CA 94305 USA

ISBN: (纸本)9781424441266

image-based blood flow modeling provides a unique way to quantify wall shear stress in 3D subject-specific geometric models. These models are constructed from medical images, inevitably using smoothing to compensate for noise and limited resolution. Moreover, the vessel is generally getting rougher due to disease or aging. However, there are few studies on influences of different levels of smoothness/roughness on wall shear stress. From the CT image of a thoracic aorta with aneurysm, we constructed 13 models: 1 original, 6 smoothed, and 6 roughened. Using computational fluid dynamics, we show qualitatively the distribution of wall shear stress varies noticeably with different levels of roughness. However, the maximal percentage difference is about 10% when averaged spatially in banded cross sections. Our study suggests the fidelity of geometry is significant in understanding the localization of wall shear stress, whereas the overall trend is relatively insensitive to the level of smoothness/roughness.

关键词： Wall shear stress vessel roughness smoothness blood flow image-based modeling

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image-based Street-side City modeling 09

Image-based Street-side City Modeling

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ACM SIGGRAPH Asia Conference 2009

作者： Xiao, Jianxiong Fang, Tian Zhao, Peng Lhuillier, Maxime Quan, Long Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781605588582

We propose an automatic approach to generate street-side 3D photo-realistic models from images captured along the streets at ground level. We first develop a multi-view semantic segmentation method that recognizes and segments each image at pixel level into semantically meaningful areas, each labeled with a specific object class, such as building, sky, ground, vegetation and car. A partition scheme is then introduced to separate buildings into independent blocks using the major line structures of the scene. Finally, for each block, we propose an inverse patch-based orthographic composition and structure analysis method for facade modeling that efficiently regularizes the noisy and missing reconstructed 3D data. Our system has the distinct advantage of producing visually compelling results by imposing strong priors of building regularity. We demonstrate the fully automatic system on a typical city example to validate our methodology.

关键词： image-based modeling street view street-side building modeling facade modeling city modeling 3D reconstruction

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