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29 results for “Image registration”
Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)
<p>This includes a volumetric whole-brain calcium recording of a freely behaving worm (<em>C. elegans</em>) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.</p>
Whole slide images of mouse liver serial sections - Test registration dataset
<p>15 H&E serial section of mouse liver and small intestine.</p> <p>Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller, Gian-Filippo Mancini, and Agnès Hautier.</p> <p>All slides where imaged with a VS200 Evident slide scanner from the<a href="http://biop.epfl.ch/"> EPFL BIOP imaging facility</a>.</p>
3D co-registration of ultra-low-field and high-field magnetic resonance images (data)
<p>Dataset used for "3D co-registration of ultra-low-field and high-field magnetic resonance images" submitted to PlosOne.</p>
Validation of the registration of intraoperative optical image of exposed brain with preoperative MRI volumes (T1 volumes with injection of Gadolinium).
<p>This dataset contains the results of the registration of intraoperative optical images of exposed brain with pre-operative MRI volumes (T1 volumes with injection of Gadolinium). The file contains the validation metric (Euclidean distance) calculated with a landmark-based validation approach for 9 patients.</p>
Unsupervised microscopy image registration datasets
<p>This repository contains the raw microscopy datasets for the article entitled "<strong>Unsupervised multimodal image registration with deep learning for biomedical microscopy".</strong></p> <p> </p>
Lung CT Deformable Image Registration Validation Dataset
<p>This dataset contains 30 different cases of CT image pairs, with a high number of vessel bifurcation landmark pairs identified in each case. These landmarks can be used for deformable image registration (DIR) algorithm validation and quality assurance. Images are obtained from several publicly available image repositories as well as clinical scans from Barnes Jewish Hospital.</p> <p> </p> <p>Guidelines for loading and visualizing data can be found on our Github at </p> <p>https://github.com/deshanyang/Lung-DIR-QA</p> <p> </p> <p>If you use our dataset, please cite the paper at </p> <p>https://doi.org/10.1002/mp.17026</p>
BIDS Data for "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging"
<p>Base data package for the “An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging” article, formatted corresponding to the Brain Imaging Data Structure.</p>
High Resolution Fundus Image Database for Monomodal Single-Channel Image Registration of Thin Features
<p>A high resolution image database of 42 image pairs (related by elastic deformations) created from original images from the High Resolution Fundus Image Database.<br> <br> Consists of thin linear structures that lack sufficient overlap to pose a challenge for classic similarity measures based on overlapping pixels commonly used in image registration.</p> <p>The dataset contains the intensity grayscale images, as well as binary masks of the retinal area, and labels of the vessels, segmented by expert annotators.<br> </p>
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>
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>
Preprocessed Data used by "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration"
<p>Data, preprocessed by the SAMRI package, used to train the models in "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration". The models can be found <a href="https://zenodo.org/record/3759361#.XrKrgBMzZhE">here</a>.</p>
Serial two-photon tomography (STPT) of the brain through bi-channel image registration and deep learning segmentation (BIRDS)
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
Test Dataset for Whole Slide Image Registration
<p>Mouse duodenum fixed in 4% PFA overnight at 4°C, processed for paraffin infiltration using a standard histology procedure and cut at 4 microns were dewaxed, rehydrated, permeabilized with 0.5% Triton X-100 in PBS 1x and stained with Azide - Alexa Fluor 555 (Thermo Fisher) to detect EdU and DAPI for nuclei. The images were taken using a Leica DM5500 microscope with a 40X N.A.1 objective (black&white camera: DFC350FXR2, pixel dimension: 0.161 microns). Next, the slide was unmounted and stained using the fully automated Ventana Discovery xT autostainer (Roche Diagnostics, Rotkreuz, Switzerland). All steps were performed on automate with Ventana solutions. Sections were pretreated with heat using the CC1 solution under mild conditions. The primary rat anti BrDU (clone: BU1/75 (ICR1), Serotec, diluted 1:300) was incubated 1 hour at 37°C. After incubation with a donkey anti rat biotin diluted 1:200 (Jackson ImmunoResearch Laboratories), chromogenic revelation was performed with DabMap kit. The section was counterstained with Harris hematoxylin (J.T. Baker) before a second round of imaging on DM5500 PL Fluotar 40X N.A.1.0 oil (color camera: DFC 320 R2, pixel dimension: 0.1725 microns). Before acquisition, a white-balance as well as a shading correction is performed according to Leica LAS software wizard. The fluorescence and DAB images were converted in ome.tiff multiresolution file with the <a href="https://github.com/BIOP/ijp-kheops">kheops Fiji Plugin</a>.</p> <p>Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller and Gian-Filippo Mancini.</p> <p>Associated documents:</p> <ul> <li><a href="https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/">https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/</a></li> <li>https://imagej.net/plugins/bdv/warpy/warpy</li> </ul> <p>This document contains a full QuPath project with an example of registered image.</p> <p> </p>
Serial two-photon tomography (STPT) of the brain through bi-channel image registration and deep learning segmentation (BIRDS)
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Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
Supplementary animation file for INSPIRE: Intensity and spatial information-based deformable image registration
<p>Illustrative animation of the INSPIRE registration method applied to a retinal image.</p>
afids-data: Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration
<p>Curated anatomical landmarks placements for common neuroimaging templates and datasets.</p>
Dataset with results of "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders"
<p><strong>Summary</strong></p> <p>This dataset contains all results from the paper "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders". Most notably, this dataset contains the corresponding CT slices for slice photographs of 1347 'Kanzi' apples. This dataset also contains data of the results section, metadata required to make the registration code run, and segmentation masks of the apple slice photographs. The "raw" data that was used to produce these results can be found in another Zenodo dataset: <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</p> <p><br><strong>Description</strong></p> <ul> <li><strong>registered ct photo side-by-side view.zip </strong>is the easiest way to explore the registered CT photo image pairs. For every apple slice it contains a .png image consisting of the slice photo, registered CT slice and a combined view (photo=green, CT=purple) side-by-side. The resolution was reduced to reduce the file size.</li> <li><strong>registered ct slices.zip </strong>contains the full resolution CT slices as .tiff files. The matching slice photos can be found in <strong>slice_photos_crop.zip</strong> in <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</li> <li><strong>photo metadata.zip </strong>contains all metadata files required to run the code on <a href="https://github.com/D1rk123/apple_photo_ct_workflow">https://github.com/D1rk123/apple_photo_ct_workflow</a>.</li> <li><strong>results.zip</strong> contains the IPCED annotations and per apple metrics that were used to calculate all the average metrics and tables in the results section of the paper.</li> <li><strong>subset experiment registered annotation slice.zip </strong>contains the full resolution CT slices of the annotation slice in the subset experiment as .tiff files.</li> <li><strong>segmentation masks.zip </strong>contains slice photo segmentation masks as .png images. There are subfolders for the training set, the test set and the masks used for the workflow in the paper.</li> </ul> <p><br><strong>Research group</strong><br>This dataset was produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p> <p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003).</p>
Patient Specific Virtual Reality for Simulation of Spine Procedures: an Intelligent Image Segmentation, Registration and 3-dimensional Visualization in a Unified Virtual Reality Workflow for Image Gui
ClinicalTrials.gov study NCT06714539. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.