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302 results for “registration”
Registration of multi-view echocardiography sequences using a subspace similarity measure
<p>Data employed for the validation of the PCA-based similarity metric proposed in "<em>Registration of multi-view echocardiography sequences using a subspace similarity measure.</em>" Peressutti <em>et al.</em> (under review).</p> <p>Data consists of echocardiography sequences of the Left ventricle of four volunteers (vol_A-vol_D) from different acoustic windows (aw_1-aw_5). The image format is metadata.<br /> For each subject, the ground-truth rigid transformations that aligns each sequence to all the others is provided. Such transformations are provided by optically tracking the position of the ultrasound imaging probe. </p> <p>For more detailed information regarding the datasets, please refer to the paper.</p> <p>Python code for testing the proposed method can be downloaded from (https://github.com/devisperessutti/Python.git), while MATLAB code can be downloaded from (https://github.com/gomezalberto/Matlab.git).</p>
Patent registration to each native species per country
<p>The dataset contains patents per species per country used in the article</p>
Zebrafish Brain Registration Utility Archive
<p>Sample brain images, atlases and scripts for larval zebrafish whole brain imaging and registration.</p>
Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Deformable Registration Validation Dataset
<p>This dataset includes the supporting input files, scripts, and output files for validation tests of lsmgridtrack v0.3 applied to resonance scanned images. </p>
Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"
<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.</p> <p>Contents:</p> <ul> <li>"fluo_training": Fluorescence microscopic channel of the 117 training images </li> <li>"sem_training": Scanning electron microscopic channel of the 117 training images</li> <li>"fluo_validation": Fluorescence microscopic channel of the 4 validation images </li> <li>"sem_validation": Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>
SPIM example dataset for multiview registration using BigStitcher
<p><strong>Registration example dataset for</strong> <strong><a href="https://imagej.net/plugins/bigstitcher/multiview-mode">BigStitcher's MultiView Mode</a></strong></p> <ul> <li>SPIM-5angle-3Ch-dataset_rescaled.zip (2.49 GB unzipped)</li> </ul> <p>This is a 5-angle SPIM dataset imaged with the Zeiss Lightsheet.Z1 featuring a polyclad flatworm juvenile of the species <em>Echinoplana celerrima</em>. The dataset includes 3 channels (405 - nuclei, 488 muscle fibres and 561 - cilia).</p> <p>Beads can be detected only in the 561 channel. The transformation of this channel can then be duplicated and applied to all other channels.</p> <p> </p>
Tendencias de investigación sobre el uso de tecnología en el servicio registral inclusivo: Un análisis bibliométrico
<p><span>The main objective of this bibliometric article is to analyse and synthesise existing research exploring the use of emerging technologies, such as blockchain and non-fungible tokens (NFTs), in the context of inclusive registry services. A bibliometric methodology was adopted, constructing a canonical search equation in the Scopus database, without temporal restrictions. The extracted data were processed using Bibliometrix and VOSviewer to create scientific maps and perform statistical analyses. The main results reveal a growing interest and scientific production around this topic, with a total of 1122 published papers and an outstanding international collaboration. Blockchain technology emerges as the predominant focus, while non-fungible tokens and legal and regulatory aspects are also central topics. An interdisciplinary nature is observed, with contributions from diverse areas of knowledge, such as computer science, social sciences and engineering. Although the interest is global, the participation of countries such as the United States, Canada and China stands out. In conclusion, the field of research on the use of technology in inclusive registry services has experienced significant growth, reflecting the importance of adopting innovative solutions to promote more transparent, efficient and accessible registry services.</span></p> <p><strong><span>Keywords:</span></strong><span> Bibliometrics, blockchain, Non fungible tokens (NFT), inclusive registry services, regulation.</span></p>
Data from: Three-dimensional infrared scanning: An enhanced approach for spatial registration of probes for neuroimaging
<p>Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations.</p> <p>Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use.</p> <p>Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants.</p> <p>Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies.</p> <p>Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans.</p>
Datasets for Purgar et al. 2024: Supporting study registration to reduce research waste
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Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation
<p>Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation. Medical Physics, 2018 Nov;45(11):5343-5351. doi: 10.1002/mp.13174.</p>
Automatic registration of magnetic resonance and optoacoustic tomography
<p>optoacoustic / photoacoustic tomography - magnetic resonance imaging registration dataset.</p> <p>1. phantom with fiducial marker inserts</p> <p>2. ex vivo mouse brain</p> <p>3. in vivo mouse brain </p> <p>4. RegOA registration application </p>
Linear Registration of brain MRI using knowledge-based multiple intermediator libraries
<p>These are the dataset materials, including full data, resampled data, transformation matrices, experimental results and quantitative evaluation for the paper “Linear Registration of brain MRI using knowledge-based multiple intermediator libraries” that is submitted on the Journal of "Frontiers in Neuroscience".</p>
Datasets for Evaluation of Multimodal Image Registration
<p><strong>Description</strong></p> <ul> <li><strong>Aerial data</strong></li> <li>The Aerial dataset is divided into 3 sub-groups by IDs: {7, 9, 20, 3, 15, 18}, {10, 1, 13, 4, 11, 6, 16}, {14, 8, 17, 5, 19, 12, 2}. Since the images vary in size, each image is subdivided into the maximal number of equal-sized non-overlapping regions such that each region can contain exactly one 300x300 px image patch. Then one 300x300 px image patch is extracted from the centre of each region. The particular 3-folded grouping followed by splitting leads to that each evaluation fold contains 72 test samples. <ul> <li> <p>Modality A: Near-Infrared (NIR)</p> </li> <li> <p>Modality B: three colour channels (in B-G-R order)</p> </li> </ul> </li> <li><strong>Cytological data</strong></li> <li>The Cytological data contains images from 3 different cell lines; all images from one cell line is treated as one fold in 3-folded cross-validation. Each image in the dataset is subdivided from 600x600 px into 2x2 patches of size 300x300 px, so that there are 420 test samples in each evaluation fold. <ul> <li> <p>Modality A: Fluorescence Images</p> </li> <li> <p>Modality B: Quantitative Phase Images (QPI)</p> </li> </ul> </li> <li><strong>Histological dataset</strong></li> <li>For the Histological data, to avoid too easy registration relying on the circular border of the TMA cores, the evaluation images are created by cutting 834x834 px patches from the centres of the original 134 TMA image pairs. <ul> <li> <p>Modality A: Second Harmonic Generation (SHG)</p> </li> <li> <p>Modality B: Bright-Field (BF)</p> </li> </ul> </li> </ul> <p>The evaluation set created from the above three publicly available 2D datasets consists of images undergone 4 levels of (rigid) transformations of increasing size of displacement. The level of transformations is determined by the size of the rotation angle θ and the displacement tx & ty, detailed in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets">this table</a>. Each image sample is transformed exactly once at each transformation level so that all levels have the same number of samples.</p> <ul> <li><strong>Radiological data</strong></li> <li>The Radiological dataset is divided into 3 sub-groups by patient IDs: {109, 106, 003, 006}, {108, 105, 007, 001}, {107, 102, 005, 009}. Since the Radiological dataset is non-isotropic (and also of varying resolution), it is resampled using B-spline interpolation to 1 mm<sup>3</sup> cubic voxels, taking explicit care to not resample twice; displaced volumes are transformed and resampled in one step. <ul> <li> <p>Modality A: T1-weighted MRI</p> </li> <li> <p>Modality B: T2-weighted MRI</p> </li> </ul> </li> </ul> <p>(Run <a href="https://github.com/MIDA-group/MultiRegEval/blob/master/utils/make_rire_patches.py"><code>make_rire_patches.py</code></a> to generate the sub-volumes.)</p> <p>Reference sub-volumes of size 210x210x70 voxels are cropped directly from centres of the (non-displaced) resampled volumes. Similarly as for the aforementioned 2D datasets, random (uniformly-distributed) transformations are composed of rotations θx, θy ∈ [-4, 4] degrees around the x- and y-axes, rotation θz ∈ [-20, 20] degrees around the z-axis, translations tx, ty ∈ [-19.6, 19.6] voxels in x and y directions and translation tz ∈ [-6.5, 6.5] voxels in z direction. 40 rigid transformations of increasing sizes of displacement are applied to each volume. Transformed sub-volumes, of size 210x210x70 voxels, are cropped from centres of the transformed and resampled volumes.</p> <p> </p> <p>In total, it contains 864 image pairs created from the aerial dataset, 5040 image pairs created from the cytological dataset, 536 image pairs created from the histological dataset, and metadata with scripts to create the 480 volume pairs from the radiological dataset. Each image pair consists of a reference patch <span class="math-tex">\(I^{\text{Ref}}\)</span> and its corresponding initial transformed patch <span class="math-tex">\(I^{\text{Init}}\)</span> in both modalities, along with the ground-truth transformation parameters to recover it.</p> <p>Scripts to calculate the registration performance and to plot the overall results can be found in <a href="https://github.com/MIDA-group/MultiRegEval">https://github.com/MIDA-group/MultiRegEval</a>, and instructions to generate more evaluation data with different settings can be found in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data">https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>In the <code>*.zip</code> files, each row in <code>{Zurich,Balvan}_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> or <code>Eliceiri_patches/patch_tlevel[1-4]/info_test.csv</code> provides the information of an image pair as follow:</p> <ul> <li> <p>Filename: identifier(ID) of the image pair</p> </li> <li> <p>X1_Ref: x-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y1_Ref: y-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X2_Ref: x-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y2_Ref: y-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X3_Ref: x-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y3_Ref: y-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X4_Ref: x-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y4_Ref: y-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X1_Trans: x-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y1_Trans: y-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X2_Trans: x-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y2_Trans: y-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X3_Trans: x-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y3_Trans: y-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X4_Trans: x-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y4_Trans: y-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Displacement: mean Euclidean distance between reference corner points and transformed corner points</p> </li> <li> <p>RelativeDisplacement: the ratio of displacement to the width/height of image patch</p> </li> <li> <p>Tx: randomly generated translation in the x-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>Ty: randomly generated translation in the y-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleDegree: randomly generated rotation in degrees to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleRad: randomly generated rotation in radian to synthesise the transformed patch I<sub>Init</sub></p> </li> </ul> <p>In addition, each row in <code>RIRE_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> has following columns:</p> <ul> <li>Z1_Ref: z-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></li> <li>Z2_Ref: z-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></li> <li>Z3_Ref: z-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></li> <li>Z4_Ref: z-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></li> <li>Z1_Trans: z-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></li> <li>Z2_Trans: z-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></li> <li>Z3_Trans: z-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></li> <li>Z4_Trans: z-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></li> <li>(...and similarly, coordinates of the 5th-8th corners)</li> <li>Tz: randomly generated translation in z-direction to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeX: randomly generated rotation around X-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadX: randomly generated rotation around X-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeY: randomly generated rotation around Y-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadY: randomly generated rotation around Y-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeZ: randomly generated rotation around Z-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadZ: randomly generated rotation around Z-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> </ul> <p> </p> <p><strong>Naming convention</strong></p> <ul> <li><strong>Aerial Data</strong> <ul> <li> <pre> zh{ID}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>zh5_03_02_R.png</code> indicates the <em>Reference </em>patch of the <em>3rd row</em> and <em>2nd column</em> cut from the image with ID <code>zh5</code>.</li> </ul> </li> <li><strong>Cytological data</strong> <ul> <li> <pre> {{cellline}_{treatment}_{fieldofview}_{iFrame}}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>PNT1A_do_1_f15_02_01_T.png</code> indicates the <em>Transformed </em>patch of the <em>2nd row</em> and <em>1st column</em> cut from the image with ID <code>PNT1A_do_1_f15</code>.</li> </ul> </li> <li><strong>Histological data</strong> <ul> <li> <pre> {ID}_{ReferenceOrTransformed}.tif</pre> </li> <li>Example: <code>1B_A4_T.tif</code> indicates the <em>Transformed </em>patch cut from the image with ID <code>1B_A4</code>.</li> </ul> </li> </ul> <ul> <li><strong>Radiological Data</strong> <ul> <li> <pre> patient_{ID}_{iTransform}_T.mhd</pre> </li> <li> <pre> patient_{ID}_R.mhd </pre> </li> <li>Example: <code>patient_003_8_T.mhd</code> indicates the sub-volume <em>Transformed </em>with the <em>8th random transformation</em> cut from the volume with patient ID <code>003</code>; <code>patient_003_R.mhd</code> indicates the <em>Reference </em>sub-volume the volume with patient ID <code>003</code>.</li> </ul> </li> </ul> <p> </p> <p>This dataset was originally produced by the authors of <em><a href="https://arxiv.org/abs/2103.16262">Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study</a></em>.</p>
Fig. 3 in To Registrations Of Bottom Macroinvertebrates, Fish, Amphibians And Reptiles Of The Upper Tskhenistskali River Basin (Georgia, Lower Svanetia)
Fig. 3. Caught specimens of the brown trout (S. trutta).
Fig. 4 in To Registrations Of Bottom Macroinvertebrates, Fish, Amphibians And Reptiles Of The Upper Tskhenistskali River Basin (Georgia, Lower Svanetia)
Fig. 4. Caught specimens of the Colchic barbel (B. escerichia). Amphibia
Fig. 1 in To Registrations Of Bottom Macroinvertebrates, Fish, Amphibians And Reptiles Of The Upper Tskhenistskali River Basin (Georgia, Lower Svanetia)
Fig. 1. Study area and sampling station.
OWI-Lab/py_fatigue: Zenodo registration
<p><strong>py-Fatigue toolbox for Fatigue assessment</strong></p> <p>It provides:</p> <ul> <li>a powerful cycle-counting implementation based on the ASTM E1049-85 rainflow method that retrieves the main class of the package: <code>CycleCount</code></li> <li>capability of storing the <code>CycleCount</code> results in a sparse format for storage and memory efficiency</li> <li>easy applicability of multiple mean stress effect correction models</li> <li>implementation of low-frequency fatigue recovery when "summing" multiple <code>CycleCount</code> instances</li> <li>fatigue analysis through the combination of SN curves and multiple damage accumulation models</li> <li>crack propagation analysis through the combination of the Paris' law and multiple crack geometries</li> <li>and more...</li> </ul> <p>Py-Fatigue is heavily based on <a href="https://numba.pydata.org/"><code>numba</code></a>, <a href="https://numpy.org/"><code>numpy</code></a> and <a href="https://pandas.pydata.org/"><code>pandas</code></a>, for the analytical part, and <a href="https://matplotlib.org/"><code>matplotlib</code></a> as well as <a href="https://plotly.com/python/"><code>plotly</code></a> for the plotting part.</p> <p>Therefore, it is highly recommended to have a look at the documentation of these packages as well.</p>
Learn2Reg Challenge: CT Lung Registration
<p>learn2reg_lungData_OriginalSize.zip contains the images used in the Learn2Reg challenge in the Lung CT task in the original resolution.</p> <p>For more information about this dataset go to: https://learn2reg.grand-challenge.org/</p> <p> </p>
Global Safety and Efficacy Registration Study of Crinecerfont in Pediatric Participants With Classic Congenital Adrenal Hyperplasia (CAHtalyst Pediatric Study)
ClinicalTrials.gov study NCT04806451. IPD Sharing: NO. Countries: 10. Publications: 1.
Global Safety and Efficacy Registration Study of Crinecerfont for Congenital Adrenal Hyperplasia
ClinicalTrials.gov study NCT04490915. IPD Sharing: NO. Countries: 18. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.