Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Wollestraat 29, Bruges (BE): high-resolution images of dry wood cores taken form a medieval floor joists, for tree-ring analysis

<ul><li>Dry-wood cores taken from historical timbers of a floor joists in the medieval building 'De Oude Steen', Wollestraat 29, Bruges (Belgium).</li><li><a href="https://id.erfgoed.net/erfgoedobjecten/29956 ">https://id.erfgoed.net/erfgoedobjecten/29956 </a></li><li>The cores were sampled at 22/02/2023 with a dry-wood borer (internal diameter 12 mm, external diameter 19 mm).</li><li>The cores were surfaced with increasingly finer sanding papers, from P60 up to P4000.</li><li>The cores were photograpphed with a Sony alpha7R IV full frame camera and FE 90 mm F/2.8G macro lens.</li><li>The<a href="https://www.wsl.ch/en/services-produkte/skippy/"> Skippy</a> system served as the image capturing platform.</li><li>The individual digital macro-photos were stitched with PTGui into a mosaic image (.tiff).</li><li>The mosaic images have a resolution of ~4 µm.</li></ul>

opencc-by-4.0Jun 2018View details →

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

This data set corresponds to the paper: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions [1] (Experiment: Comprehensive reporting). The key research questions corresponding to this data set were: RQ1: What is the role of challenges for the field of biomedical image analysis (e.g. How many challenges conducted to date? In which fields? For which algorithm categories? Based on which modalities?) RQ2: What is common practice related to challenge design (e.g. choice of metric(s) and ranking methods, number of training/test images, annotation practice etc.)? Are there common standards? RQ3: Does common practice related to challenge reporting allow for reproducibility and adequate interpretation of results? To address these research questions, we aimed to capture all biomedical image analysis challenges that have been conducted up to 2016. To acquire the data, we analyzed the websites hosting/representing biomedical image analysis challenges, namely grand-challenge.org, dreamchallenges.org and kaggle.com as well as websites of main conferences in the field of biomedical image analysis, namely Medical Image Computing and Computer Assisted Intervention (MICCAI), International Symposium on Biomedical Imaging (ISBI), International Society for Optics and Photonics (SPIE) Medical Imaging, Cross Language Evaluation Forum (CLEF), International Conference on Pattern Recognition (ICPR), The American Association of Physicists in Medicine (AAPM), the Single Molecule Localization Microscopy Symposium (SMLMS) and the BioImage Informatics Conference (BII). This yielded a list of 150 challenges with 549 tasks. Next, a tool for instantiating the challenge parameter list introduced in [1] was used by some of the authors (engineers and medical student) to formalize all challenges that met our inclusion criteria as follows: (1) Initially, each challenge was independently formalized by two different observers. (2) The formalization results were automatically compared. In ambiguous cases, when the observers could not agree on the instantiation of a parameter - a third observer was consulted, and a decision was made. When refinements to the parameter list were made, the process was repeated for missing values. Based on the formalized challenge data set, a descriptive statistical analysis was performed to characterize common practice related to challenge design and reporting. [1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., Maier, O., Maier-Hein, K., Menze, B. H., Müller, H., Neher, P. F., Niessen, W., Rajpoot, N., Sharp, G. C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Aziz Taha, A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A.: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. arXiv preprint arXiv:1806.02051 (2018).

opencc-by-4.0Oct 2018View details →

Data from Automated plankton image analysis using convolutional neural networks

Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods. Data include: 1) 42,564 item training library, sorted in 108 classes, 2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering. CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names. 3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification.  CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.   Scripts and programs: 1) Segmentation.zip contains the scripts and executables for the segmentation program. 2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet) Note that google-sparsehash is necessary for running SparseConvNet. Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.

opencc-by-4.0Aug 2019View details →

Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis

This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset. The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:   * id: unique identifier within each partition.   * tilted_label: image-level label corresponding to the tilted rectangle.   * nontilted_label: image-level label corresponding to the non-tilted rectangle.   * tilted_size: longest size of the tilted rectangle.   * nontilted_size: longest size of the non-tilted rectangle.   * kind: either "tile" or "mask" image type. The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json". The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.

opencc-by-4.0Apr 2023View details →

Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data

Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision). The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05). Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments. 10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R)

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Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) - Raw images for the analysis of stomatal density and length 2021-2022

Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the raw images underlying the data reported in a separate data package on stomatal density and length: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=372 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Jan 2025View details →

opencc-by-4.0Nov 2020View details →

Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.

The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site. The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.

opencc-by-4.0Jan 2021View details →

Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data

The dataset contains raw imaging data from the work: "Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis" The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de). Authors Lotta Hof1*, Till Moreth1*, Michael Koch1, Tim Liebisch2, Marina Kurtz3, Julia Tarnick4, Susanna M. Lissek5, Monique M.A. Verstegen6, Luc J.W. van der Laan6, Meritxell Huch7, Franziska Matthäus2, Ernst H.K. Stelzer1, Francesco Pampaloni1§ 1Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 2Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 3Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 4Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom 5Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany 6Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands 7The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany *contributed equally §corresponding author: fpampalo@bio.uni-frankfurt.de Abstract Background Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days. Results We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures. Conclusion Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.

opencc-by-4.0Sep 2023View details →

Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images

Foundational Codebook and Data:  Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip. The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models. Obstructed Class Variation: There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip. This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.

opencc-by-4.0Apr 2024View details →

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.

The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> "Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)." by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S. The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results. Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work. The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au) Thanks.

opencc-by-4.0Jan 2022View details →

Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy

Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021).  The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a>

opencc-by-4.0Mar 2022View details →

Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"

These datasets accompany the article published in Remote Sensing entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing". For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included:  <ul> <li>Raw Metrics: the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li>Rankings: the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.

opencc-by-4.0Apr 2023View details →

Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data

Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision). Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation. Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.

opencc-by-4.0Apr 2022View details →

Simulation and data analysis for "Center-of-Mass Corrections in Associated Particle Imaging"

IPython notebook used to run simulations and generate all plots the paper "Center-of-Mass Corrections in Associated Particle Imaging". The notebook also includes some more analysis and plots not shown in the paper.

opencc-by-4.0May 2024View details →

Image analysis data for Downie et al. (2025)

Image analysis outputs that can be processed using code available at <a href="https://github.com/quantixed/p065p038" target="_blank" rel="noopener">https://github.com/quantixed/p065p038</a> A preprint of the manuscript is available at <a href="https://doi.org/10.1101/2024.05.31.596797" target="_blank" rel="noopener">https://doi.org/10.1101/2024.05.31.596797</a>. <ul> <li>LBR cluster analysis (plasma membrane, 3D) - <code>SJR233</code> analysis of LD317</li> <li>LBR cluster analysis (plasma membrane, 2D movie) - <code>SJR217</code> analysis of LD295</li> <li>Mitochondria-ER contact analysis from SBF-SEM - <code>SJR242</code></li> <li>Line profile comparison - <code>SJR266</code> analysis of LD237, LD239, LD352, LD365, LD360</li> <li>Thapsigargin experiment - <code>SJR265</code> analysis of LD446</li> <li>Sec61 and LBR cluster analysis (3D) under different promoters - <code>Sec61Expr</code></li> <li>LBR cluster sizes after 2h or 4h (3D) - <code>LBR_LongTerm</code></li> </ul> All other data for plot recreation can be found in the GitHub repo.

opencc-by-4.0Nov 2023View details →

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."

opencc-by-4.0Apr 2024View details →

Dataset related to article "Phantom‑based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM"

The dataset comprises  information from several DICOM tags extracted from digital mammography (DM), digital breast tomosynthesis (DBT), and contrast-enhanced mammography (CEM) images acquired in a phantom study aimed at characterizing the automatic exposure control (AEC) behavior of diverse mammography equipment. The final ten columns of the datasets encompass signal (mena pixel values, MPV) and noise (standard deviation, SD) measurements derived from phantom images. These measurements are used to compute several image quality metrics, including contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), CNR relative difference in comparison to the 45 mm reference thickness, and a figure of merit (FOM) obtained by diving the squared CNR by the mean glandular dose (MGD).

opencc-by-4.0Dec 2023View details →

Single cell analysis by Quantitative image-based cytometry (QIBC)

Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters.  Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.