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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Berto, Giulia Avesani, Paolo Pestilli, Franco Bullock, Daniel Carona, Bradley Olivetti, Emanuele Bruno Kessler Fdn Neurolnformat Lab NILab Trento Italy Univ Trento Ctr Mind & Brain Sci CIMeC Trento Italy Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781538636411

Segmenting white matter bundles from human tractograms is a task of interest for several applications. Current methods for bundle segmentation consider either only prior knowledge about the relative anatomical position of a bundle, or only its geometrical properties. Our aim is to improve the results of segmentation by proposing a method that takes into account information about both the underlying anatomy and the geometry of bundles at the same time. To achieve this goal, we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomical information within the optimization process. The proposed method shows a significant improvement with respect to the original method, in particular on small bundles.

关键词： diffusion MRI bundle segmentation Linear Assignment Problem

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Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

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NEUROIMAGE 2021年 242卷 118451-118451页

作者： Schilling, Kurt G. Tax, Chantal M. W. Rheault, Francois Hansen, Colin Yang, Qi Yeh, Fang-Cheng Cai, Leon Anderson, Adam W. Landman, Bennett A. Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37235 USA Vanderbilt Univ Med Ctr Vanderbilt Inst Imaging Sci Nashville TN 37235 USA Cardiff Univ Cardiff Univ Brain Res Imaging Ctr CUBRIC Cardiff Wales Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37235 USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA 15260 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger th

关键词： Tractography bundle segmentation White matter Reproducibility Harmonization

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MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

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MAGNETIC RESONANCE IN MEDICINE 2021年第6期86卷 3304-3320页

作者： Cai, Leon Y. Yang, Qi Kanakaraj, Praitayini Nath, Vishwesh Newton, Allen T. Edmonson, Heidi A. Luci, Jeffrey Conrad, Benjamin N. Price, Gavin R. Hansen, Colin B. Kerley, Cailey, I Ramadass, Karthik Yeh, Fang-Cheng Kang, Hakmook Garyfallidis, Eleftherios Descoteaux, Maxime Rheault, Francois Schilling, Kurt G. Landman, Bennett A. Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN USA Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37232 USA Vanderbilt Univ Vanderbilt Univ Inst Imaging Sci Nashville TN USA Mayo Clin Dept Radiol Rochester MN USA Univ Texas Austin Dept Biomed Engn Austin TX USA Rutgers State Univ Robert Wood Johnson Med Sch Dept Psychiat New Brunswick NJ USA Vanderbilt Univ Vanderbilt Brain Inst Neurosci Grad Program Med Ctr Nashville TN USA Vanderbilt Univ Peabody Coll Dept Psychol & Human Dev Nashville TN USA Univ Pittsburgh Sch Med Dept Neurol Surg Pittsburgh PA 15261 USA Vanderbilt Univ Dept Biostat Med Ctr Nashville TN USA Indiana Univ Dept Intelligent Syst Engn Indiana PA USA Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada

Purpose: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. Methods: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm(2) across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector;region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index;bundle-wise shape, volume, fractional anisotropy, and length;and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. Results: We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. Conclusions: This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.

关键词： bundle segmentation connectome DTI NODDI reproducibility variability

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Tractostorm: The what, why, and how of tractography dissection reproducibility

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HUMAN BRAIN MAPPING 2020年第7期41卷 1859-1874页

作者： Rheault, Francois De Benedictis, Alessandro Daducci, Alessandro Maffei, Chiara Tax, Chantal M. W. Romascano, David Caverzasi, Eduardo Morency, Felix C. Corrivetti, Francesco Pestilli, Franco Girard, Gabriel Theaud, Guillaume Zemmoura, Ilyess Hau, Janice Glavin, Kelly Jordan, Kesshi M. Pomiecko, Kristofer Chamberland, Maxime Barakovic, Muhamed Goyette, Nil Poulin, Philippe Chenot, Quentin Panesar, Sandip S. Sarubbo, Silvio Petit, Laurent Descoteaux, Maxime Univ Sherbrooke SCIL Sherbrooke PQ Canada Bambino Gesu Pediat Hosp Dept Neurosci & Neurorehabil Neurosurg Unit IRCCS Rome Italy Univ Verona Dept Comp Sci Verona Italy Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Cardiff Univ Sch Psychol CUBRIC Cardiff S Glam Wales Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 Lausanne Switzerland Univ Calif San Francisco Dept Neurol San Francisco CA USA Imeka Solut Sherbrooke PQ Canada Hop Lariboisiere Dept Neurochirurg Paris France Indiana Univ Dept Psychol & Brain Sci Bloomington IN USA Univ Tours INSERM IBrain UMR 1253 Tours France San Diego State Univ Dept Psychol Brain Dev Imaging Labs San Diego CA 92182 USA Univ Pittsburgh LRDC Pittsburgh PA USA ISAE SUPAERO Toulouse France Stanford Univ Dept Neurosurg Stanford CA 94305 USA Santa Chiara Hosp Emergency Dept Div Neurosurg APSS Trento Italy Univ Bordeaux CEA CNRSUMR 5293 Grp Imagerie NeurofonctInst Malad Neurodegenerat Bordeaux France

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection." Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC-ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real-life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and "virtual dissection" across various laboratories and hospitals. Intra-rater agreement (Dice score) was approximately 0.77, while inter-rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

关键词： bundle segmentation diffusion MRI inter-rater intra-rater reproducibility tractography white matter

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Segmenting large historical notarial manuscripts into multi-page deeds

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PATTERN ANALYSIS AND APPLICATIONS 2024年第1期27卷 22-22页

作者： Prieto, Jose Ramon Becerra, David Toselli, Alejandro Hector Alonso, Carlos Vidal, Enrique Univ Politecn Valencia PRHLT Res Ctr Cami Vera S N Valencia 46021 Valencia Spain TranSkriptorium IA Valencia Spain Univ Seville Seville Spain

Archives around the world hold vast digitized series of historical manuscript books or "bundles" containing, among others, notarial records also known as "deeds" or "acts". One of the first steps to provide metadata which describe the contents of those bundles is to segment them into their individual deeds. Even if deeds are often page-aligned, as in the bundles considered in the present work, this is a time-consuming task, often prohibitive given the huge scale of the manuscript series involved. Unlike traditional Layout Analysis methods for page-level segmentation, our approach goes beyond the realm of a single-page image, providing consistent deed detection results on full bundles. This is achieved in two tightly integrated steps: first, we estimate the class-posterior at the page level for the "initial", "middle", and "final" classes;then we "decode" these posteriors applying a series of sequentiality consistency constraints to obtain a consistent book segmentation. Experiments are presented for four large historical manuscripts, varying the number of "deeds" used for training. Two metrics are introduced to assess the quality of book segmentation, one of them taking into account the loss of information entailed by segmentation errors. The problem formalization, the metrics and the empirical work significantly extend our previous works on this topic.

关键词： Handwritten document image processing Multi-page layout analysis bundle segmentation Historical manuscripts

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Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas 18

Superficial white matter shape characterization using hierar...

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18th International Symposium on Medical Information Processing and Analysis

作者： Mendoza, C. Roman, C. Molina, J. Poupon, C. Mangin, J. -F. Hernandez, C. Guevara, P. Univ Concepcion Concepcion Chile Univ Valparaiso Valparaiso Chile CEA NeuroSpin I2BM Gif Sur Yvette France Ctr Biotechnol & Bioengn CeBiB Santiago Chile

ISBN: (纸本)9781510662544;9781510662551

The description of the superficial white matter (SWM) functional and structural organization is still an un-achieved task. In particular, their shape has not been assessed in detail using diffusion Magnetic Resonance Imaging (dMRI) tractography. This work aims to characterize the different shapes of the short-range association connections present in an SWM multi-subject bundle atlas derived from probabilistic dMRI tractography datasets. First, we calculated a representative centroid shape for each atlas bundle. Next, we computed a distance matrix that encodes the similarity between every pair of centroids. For the distance matrix computation, centroids were first aligned using a streamline-based registration, reducing the 3D spatial separation effect and allowing us to focus only on shape differences. Then, we applied a hierarchical clustering algorithm over the affinity graph derived from the distance matrix. As a result, we obtained ten classes with distinctive shapes, ranging from a straight line form to U and C arrangements. The most predominant shapes were: (i) short open U, (ii) short closed U, and (iii) short C. Moreover, we used the shape information to filter out noisy streamlines in the atlas bundles and applied an automatic segmentation algorithm to 25 subjects of the HCP database. Our results show that the filtering steps help to segment more dense bundles with fewer outliers, improving the identification of the brain's short fibers.

关键词： superficial white matter tractography hierarchical clustering bundle segmentation

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A STEM-BASED DISSECTION OF INFERIOR FRONTO-OCCIPITAL FASCICULUS WITH A DEEP LEARNING MODEL 17

A STEM-BASED DISSECTION OF INFERIOR FRONTO-OCCIPITAL FASCICU...

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IEEE 17th International Symposium on Biomedical Imaging (ISBI)

作者： Astolfi, Pietro De Benedictis, Alessandro Sarubbo, Silvio Berto, Giulia Olivetti, Emanuele Sona, Diego Avesani, Paolo Bruno Kessler Fdn NeuroInformat Lab NILab Trento Italy Univ Trento Ctr Mind & Brain Sci CIMeC Trento Italy Ist Italiano Tecnol PAVIS Genoa Italy Bambino Gesu Pediat Hosp Neurosurg Unit Rome Italy S Chiara Hosp APSS Div Neurosurg Trento Italy

ISBN: (纸本)9781538693308

The aim of this work is to improve the virtual dissection of the Inferior Frontal Occipital Fasciculus (IFOF) by combining a recent insight on white matter anatomy from ex-vivo dissection and a data driven approach with a deep learning model. Current methods of tract dissection are not robust with respect to false positives and are neglecting the neuroanatomical waypoints of a given tract, like the stem. In this work we design a deep learning model to segment the stem of IFOF and we show how the dissection of the tract can be improved. The proposed method is validated on the Human Connectome Project dataset, where expert neuroanatomists segmented the IFOF on multiple subjects. In addition we compare the results to the most recent method in the literature for automatic tract dissection.

关键词： diffusion MRI bundle segmentation deep learning IFOF stem

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FIESTA: Autoencoders for accurate fiber segmentation in tractography

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NEUROIMAGE 2023年第1期279卷 120288-120288页

作者： Dumais, Felix Legarreta, Jon Haitz Lemaire, Carl Poulin, Philippe Rheault, Francois Petit, Laurent Barakovic, Muhamed Magon, Stefano Descoteaux, Maxime Jodoin, Pierre-Marc Neuroimaging, for the Alzheimer's Disease Univ Sherbrooke Dept Comp Sci Sherbrooke Connect Imaging Lab SCIL Sherbrooke PQ Canada Univ Sherbrooke Dept Comp Sci Videos & Images Theory & Analyt Lab VITAL Sherbrooke PQ Canada Harvard Med Sch Brigham & Womens Hosp Dept Radiol Mass Gen Brigham Boston MA USA Univ Sherbrooke Ctr Calcul Sci Sherbrooke PQ Canada Univ Sherbrooke Dept Comp Sci Med Imaging & Neuroinformat MINi Lab Sherbrooke PQ Canada Univ Bordeaux Grp Imagerie Neurofonct GIN CNRS CEAIMNGINUMR 5293 F-33000 Bordeaux France F Hoffmann La Roche Ltd Roche Innovat Ctr Basel Pharma Res & Early Dev Neurosci & Rare Dis Basel Switzerland Imeka Solut inc Sherbrooke PQ Canada

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as Recobundles, RecobundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework

关键词： Fiber tractography bundle segmentation Autoencoder Representation learning Generative sampling dMRI

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Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

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NEUROIMAGE 2021年 243卷 118502-118502页

作者： Schilling, Kurt G. Rheault, Francois Petit, Laurent Hansen, Colin B. Nath, Vishwesh Yeh, Fang-Cheng Girard, Gabriel Barakovic, Muhamed Rafael-Patino, Jonathan Yu, Thomas Fischi-Gomez, Elda Pizzolato, Marco Ocampo-Pineda, Mario Schiavi, Simona Canales-Rodriguez, Erick J. Daducci, Alessandro Granziera, Cristina Innocenti, Giorgio Thiran, Jean-Philippe Mancini, Laura Wastling, Stephen Cocozza, Sirio Petracca, Maria Pontillo, Giuseppe Mancini, Matteo Vos, Sjoerd B. Vakharia, Vejay N. Duncan, John S. Melero, Helena Manzanedo, Lidia Sanz-Morales, Emilio Pena-Melian, Angel Calamante, Fernando Attye, Arnaud Cabeen, Ryan P. Korobova, Laura Toga, Arthur W. Vijayakumari, Anupa Ambili Parker, Drew Verma, Ragini Radwan, Ahmed Sunaert, Stefan Emsell, Louise De Luca, Alberto Leemans, Alexander Bajada, Claude J. Haroon, Hamied Azadbakht, Hojjatollah Chamberland, Maxime Genc, Sila Tax, Chantal M. W. Yeh, Ping-Hong Srikanchana, Rujirutana Mcknight, Colin D. Yang, Joseph Yuan-Mou Chen, Jian Kelly, Claire E. Yeh, Chun-Hung Cochereau, Jerome Maller, Jerome J. Welton, Thomas Almairac, Fabien Seunarine, Kiran K. Clark, Chris A. Zhang, Fan Makris, Nikos Golby, Alexandra Rathi, Yogesh O'Donnell, Lauren J. Xia, Yihao Aydogan, Dogu Baran Shi, Yonggang Fernandes, Francisco Guerreiro Raemaekers, Mathijs Warrington, Shaun Michielse, Stijn Ramirez-Manzanares, Alonso Concha, Luis Aranda, Ramon Meraz, Mariano Rivera Lerma-Usabiaga, Garikoitz Roitman, Lucas Fekonja, Lucius S. Calarco, Navona Joseph, Michael Nakua, Hajer Voineskos, Aristotle N. Karan, Philippe Grenier, Gabrielle Legarreta, Jon Haitz Adluru, Nagesh Nair, Veena A. Prabhakaran, Vivek Alexander, Andrew L. Kamagata, Koji Saito, Yuya Uchida, Wataru Andica, Christina Abe, Masahiro Bayrak, Roza G. Wheeler-Kingshott, Claudia A. M. Gandini D'Angelo, Egidio Palesi, Fulvia Savini, Giovanni Rolandi, Nicolo Guevara, Pamela Houenou, Josselin Lopez-Lopez, Narciso Mangin, Jean-Francois Poupon, Cyril Roman, Claudio Vazquez, Andrea Maffei, Chiara Arantes, Mavilde Andrade, Jose Paulo Silva, Susana Ma Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37232 USA Univ Sherbrooke SCIL Quebec City PQ Canada Univ Bordeaux CNRS Inst Malad Neurodegenerat Grp Imagerie NeurofonctCEA Bordeaux France Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA USA CIBM Ctr BioMed Imaging Lausanne Switzerland Univ Hosp Dept Med & Biomed Engn Translat Imaging Neurol ThINK Basel Switzerland Univ Basel Basel Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab LTSS Lausanne Switzerland Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Univ Verona Dept Comp Sci Verona Italy Karolinska Inst Dept Neurosci Stockholm Sweden UCL Hosp NHS Fdn Trust Lysholm Dept Neuroradiol Natl Hosp Neurol & Neurosurg London England Univ Federico II Dept Adv Biomed Sci Naples Italy Univ Federico II Dept Neurosci & Reprod & Odontostomatol Sci Naples Italy Univ Sussex Brighton & Sussex Med Sch Dept Neurosci Brighton E Sussex England UCL Ctr Med Image Comp London England UCL Dept Clin & Expt Epilepsy London England Epilepsy Soc MRI Unit Gerrards Cross England Univ Rey Juan Carlos Lab Anal Imagen Med & Biometr LAIMBIO Madrid Spain Univ Rey Juan Carlos Fac Ciencias Salud Madrid Spain Univ Complutense Madrid Fac Med Dept Anat Madrid Spain Univ Sydney Sydney Imaging Sydney NSW Australia Univ Sydney Sch Biomed Engn Sydney NSW Australia Univ Southern Calif Keck Sch Med USC Lab Neuro Imaging Stevens Neuroimaging & Informat Inst Los Angeles CA 90007 USA Univ Southern Calif Keck Sch Med USC Stevens Neuroimaging & Informat Inst Ctr Integrat Connect Los Angeles CA 90007 USA Univ Penn Dept Radiol Philadelphia PA 19104 USA Katholieke Univ Leuven Dept Imaging & Pathol Translat MRI Leuven Belgium UMC Utrecht PROVIDI Lab Utrecht Netherlands Univ Malta Fac Med & Surg Dept Physiol & Biochem Msida Malta Univ Manchester Manchester Acad Hlth Sci Ctr Div Neurosci & Expt Psych

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

关键词： Tractography bundle segmentation White matter Fiber pathways Dissection

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Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites

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DATA IN BRIEF 2023年 48卷 109058页

作者： Mikkelsen, Lars P. Faester, Soren Dahl, Vedrana A. Tech Univ Denmark DTU Wind DK-4000 Roskilde Denmark Tech Univ Denmark DTU Compute DK-2800 Lyngby Denmark Tech Univ Denmark DTU Wind Energy Riso CampusFrederiksborgvej 399 DK-4000 Roskilde Denmark

This dataset includes four large field-of-view scanning elec-tron microscopy (SEM) images together with associated Mat -lab scripts aimed for the analysis used in the joint publi-cation. Each of the four stitched images is generated from a large number (between 15500 and 24500) high-resolution (195nm/pixel) scans, which have been stitched into four im-ages stored as tiff-files. The images show the cross-section of fiber bundles in composite laminate and are well-suited for local fiber volume determination. The image resolution corre-sponds to between 600 and 2000 pixels covering each fiber. The imaged samples are from composite laminates with an overall fiber volume fraction in the range of 55% to 60%. The local fiber volume fraction is found both for the full cross-section, as an average fiber volume fraction over the individ-ual bundles, and as a local fiber volume fraction found in a moving averaging box with a size corresponding to 5 x5 fiber diameter (80 x80 mu m2) areas. (c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： bundle segmentation SEM Fiber volume fraction Wind turbine blades

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