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      A computational framework for canonical holistic morphometric analysis of trabecular bone

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          Abstract

          Bone is a remarkable, living tissue that functionally adapts to external loading. Therefore, bone shape and internal structure carry information relevant to many disciplines, including medicine, forensic science, and anthropology. However, morphometric comparisons of homologous regions across different individuals or groups are still challenging. In this study, two methods were combined to quantify such differences: (1) Holistic morphometric analysis (HMA) was used to quantify morphometric values in each bone, (2) which could then be mapped to a volumetric mesh of a canonical bone created by a statistical free-form deformation model (SDM). Required parameters for this canonical holistic morphometric analysis (cHMA) method were identified and the robustness of the method was evaluated. The robustness studies showed that the SDM converged after one to two iterations, had only a marginal bias towards the chosen starting image, and could handle large shape differences seen in bones of different species. Case studies were performed on metacarpal bones and proximal femora of different primate species to confirm prior study results. The differences between species could be visualised and statistically analysed in both case studies. cHMA provides a framework for performing quantitative comparisons of different morphometric quantities across individuals or groups. These comparisons facilitate investigation of the relationship between spatial morphometric variations and function or pathology, or both.

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          Most cited references64

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Guidelines for assessment of bone microstructure in rodents using micro-computed tomography.

            Use of high-resolution micro-computed tomography (microCT) imaging to assess trabecular and cortical bone morphology has grown immensely. There are several commercially available microCT systems, each with different approaches to image acquisition, evaluation, and reporting of outcomes. This lack of consistency makes it difficult to interpret reported results and to compare findings across different studies. This article addresses this critical need for standardized terminology and consistent reporting of parameters related to image acquisition and analysis, and key outcome assessments, particularly with respect to ex vivo analysis of rodent specimens. Thus the guidelines herein provide recommendations regarding (1) standardized terminology and units, (2) information to be included in describing the methods for a given experiment, and (3) a minimal set of outcome variables that should be reported. Whereas the specific research objective will determine the experimental design, these guidelines are intended to ensure accurate and consistent reporting of microCT-derived bone morphometry and density measurements. In particular, the methods section for papers that present microCT-based outcomes must include details of the following scan aspects: (1) image acquisition, including the scanning medium, X-ray tube potential, and voxel size, as well as clear descriptions of the size and location of the volume of interest and the method used to delineate trabecular and cortical bone regions, and (2) image processing, including the algorithms used for image filtration and the approach used for image segmentation. Morphometric analyses should be based on 3D algorithms that do not rely on assumptions about the underlying structure whenever possible. When reporting microCT results, the minimal set of variables that should be used to describe trabecular bone morphometry includes bone volume fraction and trabecular number, thickness, and separation. The minimal set of variables that should be used to describe cortical bone morphometry includes total cross-sectional area, cortical bone area, cortical bone area fraction, and cortical thickness. Other variables also may be appropriate depending on the research question and technical quality of the scan. Standard nomenclature, outlined in this article, should be followed for reporting of results. 2010 American Society for Bone and Mineral Research.
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              Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

              Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. Result First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. Conclusion We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.
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                Author and article information

                Contributors
                bachmann@ilsb.tuwien.ac.at
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 March 2022
                25 March 2022
                2022
                : 12
                : 5187
                Affiliations
                [1 ]GRID grid.5329.d, ISNI 0000 0001 2348 4034, Institute of Lightweight Design and Structural Biomechanics, , TU Wien, ; Vienna, Austria
                [2 ]GRID grid.9759.2, ISNI 0000 0001 2232 2818, School of Anthropology and Conservation, Skeletal Biology Research Centre, , University of Kent, ; Canterbury, UK
                [3 ]GRID grid.419518.0, ISNI 0000 0001 2159 1813, Department of Human Evolution, , Max Planck Institute for Evolutionary Anthropology, ; Leipzig, Germany
                [4 ]GRID grid.459693.4, Department of Anatomy and Biomechanics, Division Biomechanics, , Karl Landsteiner University of Health Sciences, ; Krems, Austria
                Article
                9063
                10.1038/s41598-022-09063-6
                8956643
                35338187
                a9c47c04-adab-4926-a5cf-9d9af6126542
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 October 2021
                : 14 March 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 819960
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                anthropology,image processing,biomedical engineering,bone
                Uncategorized
                anthropology, image processing, biomedical engineering, bone

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