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Fig. 6 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 6. Zospeum intermedium Jochum & Ruthensteiner sp. nov. (RMNH.MOL.234132) from Gittenberger (1975). A. Light microscopic images of apertural and dorsal views. B. Sample labels. C–H. 3D visualizations of X-ray Micro-CT data. C. Aperture view. D. Aperture facing right view. E. Apical view. F. Dorsal view. G. Aperture facing right view. H. Umbilical view showing slight umbilical depression and lamella projecting from the columellar side.
Fig. 7 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 7. Light microscopic images of shells from the caves Lipska pećina and Duboki do (Montenegro) with specimen labels. A–C. Zospeum troglobalcanicum Absolon, 1916 (MCSMNH-PMSL-Moll.-FVelkovrh 29603). D–F. Zospeum dubokidoense Jochum & Ruthensteiner sp. nov. D. Holotype (MCSMNHPMSL-Moll.-FVelkovrh 30360[spm1]). E–F. Paratypes, Duboki do, Montenegro (MCSMNH-PMSLMoll.-FVelkovrh 30360[spm2-3]).
Fig. 2. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 2. 3D visualizations of Micro-CT data of the lectotype of Zospeum troglobalcanicum Absolon, 1916 (NHMW Moll.Coll.Edlauer 32.749) with shell parts and terms indicated. A. Measurements include shell height (sh), shell width (sw), aperture height (ah), aperture width (aw), height of last whorl (hlw) and spire angle (SA). B. Internal view showing columella (co), lamella (la), peristome (pe) and penultimate whorl (pw). C. Apical view showing protoconch (pr), teleoconch (te) and peristome (pe). D. Ventral view showing peristome configuration (pe) parietal shield (ps) and umbilicus (um).
Fig. 4. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 4. 3D visualizations using Micro-CT data of shells from the caves Benetina pećina (SE Bosnia and Herzegovina) and Cetinska pećina (Montenegro). A–F. Zospeum troglobalcanicum Absolon, 1916, lectotype (NHMW Moll.Coll.Edlauer 32.749). A. Aperture view. B. Aperture facing right view. C. Apical view. D. Dorsal view. E. Aperture facing left view. F. Ventral view showing umbilical depression. G–L. Zospeum tortuosum Jochum & Ruthensteiner sp. nov., holotype (MCSMNH-PMSLMoll.-FVelkovrh 34099[spm1]). G. Aperture view. H. Aperture facing right view. I. Apical view. J. Dorsal view. K. Aperture facing left view. L. Ventral view showing callused peristome positioned mostly on top of umbilicus.
Fig. 15 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 15. Zospeum biokovoense Jochum & Ruthensteiner sp. nov. (NHMW Mol.Coll.Edlauer 16.390). A–B. Light microscopic images showing aperture and dorsal views and collection labels. A. Juvenile shell. B. Holotype. C–H. 3D visualizations of Micro-CT data of holotype. C. Aperture view showing gnarled deformation of the columella. D. Aperture facing right view showing fat columella and lamella. E. Apical view. F. Dorsal view showing lopsided spire and incomplete lamella on top of fat columella. G. Aperture facing left view. H. Ventral view showing puckered umbilical depression.
Fig. 5. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 5. 3D visualizations of Micro-CT data of the deformed shell, (MCSMNH-PMSL-Moll.-FVelkovrh 34099[spm4]) from Cetinska pećina. A. Aperture view. B. Aperture facing right view. C. Apical view. D. Dorsal view. E. Aperture facing left view. F. Ventral view with umbilicus covered by last quarter of shell whorl.
Fig. 1 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 1. Map showing Western Balkan cave localities of populations of Zospeum Bourguignat, 1856. Cave localities are indicated in red.
Fig. 3 in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 3. Light microscopic images of shells from the caves Benetina pećina (SE Bosnia and Herzegovina) and Cetinska pećina (Montenegro) with specimen labels. A. Zospeum troglobalcanicum Absolon, 1916, lectotype (NHMW Moll.Coll.Edlauer 32.749) from Benetina pećina. B–C. Zospeum tortuosum Jochum & Ruthensteiner sp. nov., paratypes (MCSMNH-PMSL-Moll.-FVelkovrh 34099[spm2-3]) from Cetinska pećina. D. Zospeum tortuosum Jochum & Ruthensteiner sp. nov., holotype (MCSMNHPMSL-Moll.-FVelkovrh 34099[spm1]) from Cetinska pećina. E. Deformed shell (MCSMNH-PMSLMoll.-FVelkovrh 34099[spm4]) from Cetinska pećina.
Fig. 14. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 14. 3D visualizations of Micro-CT data of Zospeum constrictum Jochum & Ruthensteiner sp. nov. (NHMW Mol.Coll.Edlauer 16.693). A–F. Holotype. A. Aperture and dorsal views. B. Aperture facing right view. C. Apical view showing upper right side of final whorl tucked underneath spire. D. Dorsal view. E. Aperture facing left view. F. Ventral view. G–L. Paratype (Fig. 13B). G. Aperture view. H. Aperture facing right view. I. Apical view showing upper right side of final whorl tucked underneath spire. J. Dorsal view. K. Aperture facing left view. L. Ventral view.
Fig. 20. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 20. 3D visualizations of Micro-CT data of Zospeum sp. 2, Taleža pećina, bei Trebinje, BiH, presented as Z. troglobalcanicum in Inäbnit et al. 2021 (NMBE 553414/1 (ex RSC 1981). A. Aperture view showing low, ridge-like fold on inner penultimate whorl. B. Aperture facing right view showing smooth columella. C. Apical view. D. Dorsal view. E. Aperture facing right view. F. Ventral view showing peristome edge directly parallel to umbilical depression.
Fig. 8. 3D in 3D X-ray microscopy (Micro-CT) and SEM reveal Zospeum troglobalcanicum Absolon, 1916 and allied species from the Western Balkans (Ellobioidea: Carychiidae)
Fig. 8. 3D visualizations of Micro-CT data of shells from the caves Lipska pećina and Duboki do (Montenegro). A–F. Zospeum troglobalcanicum Absolon, 1916 (MCSMNH-PMSL-Moll.-FVelkovrh 29603) (Fig. 7A). A. Aperture view. B. Aperture facing right view. C. Apical view. D. Dorsal view. E. Aperture facing left view. F. Ventral view showing reduced umbilical depression. G–L. Zospeum dubokidoense Jochum & Ruthensteiner sp. nov., holotype (MCSMNH-PMSL-Moll.-FVelkovrh 30360[spm1]). G. Aperture view. H. Aperture facing right view. I. Apical view. J. Dorsal view. K. Aperture facing left view. L. Ventral view showing no umbilical depression.
Mapping of OrphaCodes to SNOMED CT with the help of Usagi
<p>The CSV file contains the mapping of OrphaCodes to SNOMED CT carried out with Usagi.<br>This file has not yet been quality assured, but will be made available for validation.</p>
PENGWIN Task 1: Pelvic Fracture Segmentation on CT
<p>The CT segmentation task (Task 1) of the <a href="https://pengwin.grand-challenge.org/">PENGWIN segmentation challenge</a> is designed to advance the development of automated fracture segmentation methods for pelvic CT scans, with a focus on enhancing their accuracy and efficiency. Our dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning equipment. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts. </p> <p>This repository contains the training set of 100 CT scans with pelvic fractures and their ground-truth segmentation labels. The images and labels are stored in mha format. Each bone anatomy (sacrum, left hipbone, right hipbone) has up to 10 fragments. Bone that does not present any fracuture has only one fragment, which is itself. Label assignment: 0 = background, 1-10 = sacrum fragment, 11-20 = left hipbone fragment, 21-30 = right hipbone fragment. </p> <p>For more information, please visit <a href="https://pengwin.grand-challenge.org/">the challenge webpage</a>. For the PENGWIN simulated X-ray training dataset (Task 2), please visit <a href="10.5281/zenodo.10913196">the separate repository</a> (10.5281/zenodo.10913196).</p>
Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
<p><strong>Abstract</strong></p> <p>Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (<em>test-GT</em>) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding <em>test-GT</em> samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.</p> <p> </p> <p><strong>Methods</strong></p> <p><strong><em>Manual Generation of test-GT</em></strong></p> <p>The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience.</p> <p><strong><em>Segmentation of Coronary Arteries using a Deep Learning Model</em></strong></p> <p>For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available:</p> <ul> <li><span>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater.</span> A sample belonging to the <em>test-GT</em> set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding <em>test-GT</em> sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned <em>test-GT</em> sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are 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). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
Smartphone imaging repository: a novel method for creating a CT image bank
<p>In a submitted manuscript, test an image capture method using smartphone camera video-derived images of brain computed tomography (CT) scans of traumatic intracranial hemorrhage. The deidentified videos are emailed or uploaded from the emergency department for central adjudication.</p> <p>We measured the time in seconds it took to capture and send the files. The primary outcomes were hematoma volume measured by ABC/2, Marshall Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture. A radiologist and an imaging scientist applied ABC/2 method, calculated the Marshall scale and midline shift on the data acquired on different smartphones and the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.</p>
ClinSpEn-CT (Clinical Terms) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Terms sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: term number and Spanish term. The direction of this sub-track is ES>EN. The used terms were directly extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
Supplementary material S2. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus (without legs).
<p>X-ray micro-CT volume rendering of the habitus (without legs) of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
Supplementary material S1. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus.
<p>X-ray micro-CT volume rendering of the habitus of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
Supplementary material S3. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the right antenna.
<p>X-ray micro-CT volume rendering of the right antenna of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
ScienceDex guides
Understand access before you commit
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.