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Bone microstructure analysis tool for photon-counting CT images using Python
No full text<div>
<h1>Image Analysis</h1>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#image-analysis"></a></div>
<p>Using Python scripts as an open source image analysis tool which can deal with images of human knee photon-counting CT and thus confirm the bone microstructure imaging. This tool mainly looks at three image processing steps, namely the extraction of the bone mask required for the registration, 3D Adaptive thresholding, subchondral bone plate segmentation, and 3D analysis where the tools main focus lies on trabecular bone parameters. The aim of these Python implementations was to provide alternatives and comparisons to the calculations performed in CTAn, allowing for a comprehensive analysis of trabecular bone morphology.</p>
<div>
<h2>Required packages</h2>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#required-packages"></a></div>
<ul>
<li>numpy</li>
<li>imagej</li>
<li>cv2 (OpenCV)</li>
<li>natsort</li>
<li>glob</li>
<li>SimpleITK</li>
<li>itk</li>
<li>porespy</li>
<li>scyjava</li>
<li>xlsxwriter</li>
<li>warnings</li>
<li>napari</li>
<li>matplotlib.pyplot</li>
<li>skimage.segmentation</li>
<li>skimage</li>
<li>gc</li>
</ul>
<div>
<h2>Creating a bone mask</h2>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#creating-a-bone-mask"></a></div>
<p>The BoneMask script operates as follows:</p>
<ul>
<li>Read the bmp images using the OpenCV function "imread".</li>
<li>Use the glob module to locate and consolidate all .bmp files in the input folder into a list.</li>
<li>Convert the list to a numpy array for simultaneous processing of the images.</li>
<li>Threshold the 3D image using the SimpleITK function Threshold.</li>
<li>Separate the different bones using the ConnectedComponent image filter from SimpleITK.</li>
<li>Order the labels according to the size of the objects using the RelabelComponent function.</li>
<li>Segment the four desired bones (femur, tibia, fibula, and patella).</li>
<li>Apply a bilateral filter to remove remaining speckles and noise.</li>
<li>Implement the shrink-wrap plugin from CTAn using an active contour algorithm.</li>
<li>Preprocess the image by enhancing the visibility of the contours with inverse Gaussian gradient.</li>
</ul>
<p>The morphological geodesic active contour function then starts from a defined region of interest (ROI) and shrinks the boundaries to fit the bone boundaries. The process is accelerated by performing morphological dilation and connectivity filtering on the thresholded and despeckled image. Finally, the morphological_geodesic_active_contour function is called with the necessary parameters to segment the bone contours.</p>
<p>The bone masking procedure can be called in the terminal using following input: <code>python BoneMask.py <link to input folder> <threshold></code> The user gives as first input the link to the folder of the bmp files for which they want to create bone masks and then, as second input, a global threshold that needs to be performed in able to segement the bone masks.</p>
<div>
<h2>3D Adaptive thresholding</h2>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#3d-adaptive-thresholding"></a></div>
<p>3D adaptive thresholding provides more accurate trabecular bone morphology. However, existing packages like OpenCV and skimage do not have the required functionality for calculating the mean of the min and max intensity levels or processing 3D images. Therefore, a custom implementation was developed. The process involves loading CT scan slices, pre-thresholding, creating cubes around each voxel to calculate threshold values, and handling edge cases.</p>
<p>"Adaptivethresh3D.py," utilizes SimpleITK, Numpy, OpenCV, and Skimage libraries. It performs 3D adaptive thresholding and can be called from the terminal with inputs including the folder link, radius, and threshold: <code>python Adaptivethresh3D.py <link to input folder> <radius> <threshold></code>. The output, thresholded images, will be saved in a subfolder named "3D_adaptive_thresh" within the input folder.</p>
<div>
<h2>Separating subchondral bone plate (SBP) from underneath subchondral trabecular bone (STB)</h2>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#separating-subchondral-bone-plate-sbp-from-underneath-subchondral-trabecular-bone-stb"></a></div>
<p>It involves 2D adaptive thresholding, separation of SBP, and the creation of periosteal and endosteal masks. Various image processing techniques such as dilation, subtraction, and morphological closing are used. The resulting masks are saved as BMP files for further analysis. "Separation_SBP.py" follows specific steps using SimpleITK, Numpy, and OpenCV libraries. It can be executed from the terminal with inputs such as the folder link, bone type (femur, tibia, or both), radius, kernel sizes, and threshold values: <code>python Separation_SBP.py <link to input folder> <bone> <radius> <kernel sizes> <threshold></code>. The output will be stored in subfolders named "bone_STB" and "bone_wholemask" within the input folder. </p>
<div>
<h2>3D Analysis</h2>
<a href="https://github.com/fahimehazari/BoneMicrostructureAanalysisTool#3d-analysis"></a></div>
<p>This section provides a detailed explanation of Python-implemented methods for calculating morphometric parameters such as bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), and trabecular number (Tb.N). The Tb.Th and Tb.Sp parameters are grouped together as they share the same calculation method, with the only difference being the use of a binary inverted image for Tb.Sp.</p>
<p>BV/TV: The BV/TV calculation was implemented in Python using the PyimageJ module, which allows running BoneJ's volume fraction function in a headless environment. The implementation is based on voxel counting, comparing the number of foreground voxels (representing bone) to the total number of voxels in the image.</p>
<p>Trabecular thickness and trabecular separation: Various implementations were compared with CTAn for calculating Tb.Th and Tb.Sp. These include the Local Thickness plugin from ImageJ, the ITKBoneMorphometry module, the PoreSpy library, the SPECTRA collaboration's code, and the deep learning-based segmentation method from Panfilov et al. Each method utilizes different algorithms, such as distance transformation, sphere fitting, and skeleton modeling, to calculate the thickness and separation values.</p>
<p>Trabecular number: Tb.N is calculated using the formula (BV/TV)/Tb.Th. The open-source tool implements Tb.N calculation based on the computed Tb.Th values from different implementations.</p>
<ul>
<li>
<p>The script is called "3D_analysis.py,". To execute the procedure from the terminal, the user needs to provide the input folder link and the voxel size as arguments: <code>python 3D_analysis.py <link to input folder> <voxel_size></code>. The output calculations are saved in an Excel file named "3D_analysis_PYTHON" within the input folder. The voxel size is specified in millimeters to allow compatibility with different CT modalities.</p>
</li>
<li>
<p>To use different implementations, specific steps must be followed. Running BoneJ headlessly from Python requires downloading ImageJ or Fiji software and installing the BoneJ plugins through Fiji. The script also needs modification, specifically adjusting the <code>os.environ['JAVA_HOME']</code> line and the <code>ij = imagej.init()</code> line to match the location of the jre file of the Fiji app. PyimageJ wrapper is used to enable BoneJ usage from Python, and installation instructions can be found on the <a href="https://github.com/imagej/pyimagej">PyimageJ GitHub page</a>.</p>
</li>
<li>
<p>Porespy and ITKBoneMorphometry methods can be installed using pip by running <code>pip install porespy</code> and <code>pip install itk-bonemorphometry</code> in the terminal, respectively.</p>
</li>
<li>
<p>The ORMIR method can be accessed on their <a href="https://github.com/SpectraCollab/ORMIR_XCT">GitHub page</a>. To download and install it using Git Bash, specific commands need to be executed.</p>
</li>
<li>
<p>For the MIPT_OULU implementations, the code is available on their <a href="https://github.com/MIPT-Oulu/SubregionalCartilageAnalysis">GitHub page</a>.</p>
</li>
</ul>
<p>Note that the installation steps mentioned above are only required for the initial use of the script.</p>
Correlation between tibial and femoral bone and cartilage changes in end-stage knee osteoarthritis
No full textKnee osteoarthritis is a whole joint disease highlighting the coupling of cartilage and bone adaptations. However, the structural properties of the subchondral bone plate (SBP) and underlying subchondral trabecular bone (STB) in the femoral compartment have received less attention compared to the tibial side. Furthermore, how the properties in the femoral compartment relate to those in the corresponding tibial site is unknown. Therefore, this study aimed to quantify the structural bone and cartilage morphology in the femoral compartment and investigate its association with those of the tibial plateau. Specifically, tibial plateaus and femoral condyles were retrieved from 28 patients with end-stage knee-osteoarthritis (OA) and varus deformity. The medial condyle of tibial plateaus and the distal part of the medial femoral condyles were micro-CT scanned (20.1 mu m/voxel). Cartilage thickness (Cart.Th), SBP, and STB microarchitecture were quantified. Significant (P < <.001; 0.79 <= r <= 0.97) correlations with a relative difference within 10% were found between the medial side of the femoral and tibial compartments. The highest correlations were found for SBP porosity (r = 0.97, mean absolute difference of 0.50%, and mean relative difference of 9.41%) and Cart.Th (r = 0.96, mean absolute difference of 0.18 mm, and relative difference of 7.08%). The lowest correlation was found for trabecular thickness (r = 0.79, mean absolute difference of 21.07 mu m, and mean relative difference of 5.17%) and trabecular number (r = 0.79, mean absolute difference of 0.18 mm-(1), and relative difference of 5.02%). These findings suggest that the distal femur is affected by OA in a similar way as the proximal tibia. Given that bone adaptation is a response to local mechanical forces, our results suggest that varus deformity similarly affects the stress distribution of the medial tibial plateau and the medial distal femur.This study is part of Limburg Clinical Research Center supported by Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital and was funded in part by KU Leuven Internal Funds (C24/16/027) and by the FWO and F.R.S.-FNRS under the Excellence of Science (EOS) programme (EOS No. 40007553)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
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Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
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koamabayili/VECTRON-author-checklist: VECTRON author checklist
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Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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