Find research datasets worth reusing

openmit-licenseJul 2019View details →

Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets

This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++.  The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets.  The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well.  This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 .   The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906. Format of the data: Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality.  The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset.  The pre-processing set is CassavaSlices.  The output set for Soybean is SoybeanResultsJul11.  The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C.  _B is the largest, and only contains the results overwritten on the original X-Ray images.  Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.325 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169065_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 1.625 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169067_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 2.6 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169068_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X DIAD X-Ray Diffraction Computed Tomography - 25 and 50 micron spot-size

This repository contains X-Ray Diffraction Computed Tomography (XRD-CT) data of a zinc doped zeolite 13X sample on the Dual Imaging and Diffraction (DIAD / K11) at Diamond Light Source. XRD-CT data is provided at a diffraction spot size of 25 microns for three region of interest slices, with a dataset size of 40x2000x80. Both the raw and reconstructed data is provided, along with the code to perform the reconstructions.  XRD-CT data is also provided at a diffraction spot size of 50 microns for a full 1.05mm volume, with a dataset size of 20x2000x40. Scans start at 43336 and finish at 43401, with a movement of 0.05mm vertically upwards between each scan. The raw and reconstructed data is provided, along with the code used to perform the reconstructions. Note: Scan 43401 is excluded as a phase-based reconstruction could not be performed. Powder X-Ray Diffraction data can be found in an alternative repository at 10.5281/zenodo.13329670 which provides the q-values of the peaks for both the Zn and Na phase to allow the best reconstructions. A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.8125 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169066_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

opencc-by-4.0Dec 2022View details →

AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or  organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2].  We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5]. We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data. IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the dcmqi [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using dcmqi and highdicom [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection.  The dataset is available in IDC, and is accompanied by our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/">repository</a> and accompanying <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/blob/main/usage_notebooks/scientific_data_paper_usage_notes.ipynb">Google Colaboratory notebook</a>.  The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.  <ul> <li>nsclc <ul> <li>2d-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_lowres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_fullres-tta  <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_gt <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.  <ul> <li>nlst <ul> <li>3d_fullres-tta  <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> The query used for NSCLC-Radiomics is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NSCLC_Radiomics_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nsclc_radiomics_series_analyzed.csv">here</a>. The query used for NLST is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NLST_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nlst_series_analyzed.csv">here</a>. The two csv files that describe the series analyzed, nsclc_series_analyzed.csv and nlst_series_analyzed.csv, are also available as uploads to this repository.  Version updates:  Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling. Version 3: Added the two csv files that describe which series were analyzed  Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units.

opencc-by-4.0Oct 2020View details →

X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats

X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats, Utah.

opencc-by-4.0Aug 2017View details →

Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading

Ex-situ X-ray CT fatigue testing data sets published as a data in brief: "Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading", Data in brief, 2017, doi.org/10.1016/j.dib.2017.10.074. Together with the following article: K. M. Jespersen and L. P. Mikkelsen, “Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades,” Compos. Sci. Technol. (In press), 2017, 10.1016/j.compscitech.2017.10.004.

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."

FIGURE 5 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 5. Inner layer of Concavicaris woodfordi (Cooper, 1932). A–C, anterior part of the specimen. A, tomogram (transversal slice). B, tomogram (transversal slice; colour-marked). C, anterior view (3D rendering). D–F, posterior part of the specimen. D, tomogram (transversal slice). E, tomogram (transversal slice; colour-marked). F, cross-section (3D rendering). G, cross-section of cephalothorax of a reptantian decapod (after Glaessner, 1969). H, I, cross-section of the carapace structure of a myodocopan (Euphilomedes japonica (Müller, 1890); after Yamada, 2019). H, attached region. I, duplicated region. Arrow indicates the rotation of the posterior part of the inner layer. Abbreviations: am, adductor muscles; app, appendage; cb, chitinous body; ef, epimeral fold; g, gills; hi, hinge; il, inner layer of the shield; ila, anterior part of the inner layer; ilp, posterior part of the inner layer; mua, attractor muscles; ol, outer layer of the shield; pl, pleural part; pta, posterior trunk appendages; so, shield outline; st, sternal part; te, tergal part. Scales: 5 mm.

FIGURE 2 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 2. General view of Concavicaris woodfordi (Cooper, 1932). A, B, Right and left lateral views. C, line drawing (lateral view). D, location of virtual slices presented in the figures (dorsal view). Abbreviations: vld, ventro-lateral depression. Arrows indicate the anterior side of the specimen. Yellow doted lines indicate longitudinal sections. Green dotted lines indicate transversal sections. Scales: 10 mm. Photos: T. A. Hegna.

FIGURE 8 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 8. Muscular structures of Concavicaris woodfordi (Cooper, 1932). A, tomogram. B, tomogram (colourmarked). C, gastric muscles (3D rendering). D, cross-section (3D rendering). E, adductor muscles (3D rendering). Abbreviations: am, adductor muscle; gm, gastric muscles; il, inner layer; lvp, latero-ventral pouch; s, shield; sto, stomach. Scales: A, B, D, 5 mm; C, E, 2 mm.

FIGURE 1 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 1. Position and geology of the fossil locality. A, Map of USA with the position of Oklahoma (red area) and of Arbuckle Mountains (grey area). B, Map of Arbuckle mountains with the position of the type locality of Concavicaris woodfordi (Cooper, 1932). C, Section of the upper Woodford Shale at Interstate 35 road-cut section (I-35) (sec. 25, T2S, R2E, Arbuckle Mountains, Oklahoma, USA; redrawn after Over [1992]).

FIGURE 4 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 4. Anatomy of Concavicaris woodfordi (Cooper, 1932). A, longitudinal virtual section. B, longitudinal virtual section (colour-marked). C, dorsal view (3D rendering). D, lateral view (3D rendering). Abbreviations: am, adductor muscles; cs, cylindrical structure; g2–7, gills; gm, gastric muscles;ila, anterior part of the inner layer; ilp, posterior part of the inner layer; lvp, latero-ventral pouch; pta, posterior trunk appendages; ra1–3, raptorial appendages; so, shield outline; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: 10 mm.

FIGURE 3 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 3. Marginal fold of Concavicaris woodfordi (Cooper, 1932). A, B, anterior part of the shield (tomogram and drawing). C, close-up of marginal fold in the anterior part of the shield (tomogram). D, E, middle part of the shield (tomogram and drawing). F, close-up of marginal fold in the middle part of the shield (tomogram). Abbreviations: il, inner layer; mf; marginal fold; so, shield outline. Scales: A, B, D, E, 5 mm; C, F, 1 mm.

FIGURE 7 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 7. Digestive, reproductive, and circulatory systems of Concavicaris woodfordi (Cooper, 1932). A, longitudinal virtual section. B, longitudinal virtual section (colour-marked). C, D, digestive and reproductive systems (3D rendering). C, anterior view. D, right lateral view. E, F, G, circulatory system. E, tomogram. F, tomogram (colour-marked). G, 3D rendering of the left part. Abbreviations: cs, cylindrical structure; g1–8, gills; go, gonads; il, inner layer; lvp, lateroventral pouch; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: A, B, 10 mm; C–G, 5 mm.

FIGURE 6 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 6. Internal anatomy of Concavicaris woodfordi (Cooper, 1932). A, right lateral view (3D rendering). B, anterior view (3D rendering). C, dorsal view (3D rendering). D, longitudinal section (3D rendering). Abbreviations: am, adductor muscles; cs, cylindrical structure; g1–8, gills; gm, gastric muscles; go, gonads; lvp, latero-ventral pouch; pta1–3, posterior trunk appendages; r, rostrum; ra1, 3, raptorial appendages; s, shield; so, shield outline; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: A, C, D, 10 mm; B, 5 mm.

FIGURE 10 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 10. Hypothetical reconstruction of Concavicaris woodfordi (Cooper, 1932). Morphology of anterior and posterior sides of the shield, eyes and number of posterior trunk appendages are reconstructed based on Concavicaris submarinus (Jobbins et al., 2020). Abbreviations: ce, compound eyes; g, gills; go, gonads; il, inner layer: lvp, lateroventral pouch; pt, posterior trunk; pta, posterior trunk appendages; r, rostrum; ra, raptorial appendages; s, shield; sto, stomach. Arrow indicates the anterior side of the specimen. Scales: 10 mm.

FIGURE 9 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 9. Appendages of Concavicaris woodfordi (Cooper, 1932). A, longitudinal section (3D rendering). B, raptorial appendages (3D rendering). C, tomogram. D, close-up of third posterior trunk appendage. E, posterior trunk appendages (3D rendering). Arrow indicates the third posterior trunk appendage. Abbreviations: il, inner layer; pta1–3, posterior trunk appendages; ra1–3, raptorial appendages; s, shield. Arrow indicates the anterior side of the specimen. Scales: A–C, 10 mm; D, G, 2 mm; E, 5 mm; F, 1 mm.