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42 results for “Abdominal CT”
Multi-organ Abdominal CT Reference Standard Segmentations
<p>DenseVNet Multi-organ Segmentation on Abdominal CT</p> <p>This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" <a href="#1">[1]</a>.</p> <p>The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the spleen, left kidney, gallbladder, esophagus, liver, stomach, pancreas and duodenum.</p> <p>The abdominal CT images and some of the reference segmentations were drawn from two data sets: <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">The Cancer Image Archive (TCIA) Pancreas-CT data set</a> [<a href="#2">2</a>-<a href="#4">4</a>] and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> [<a href="#5">5</a>-<a href="#6">6</a>]. The Pancreas-CT data set comprises abdominal CT acquired at the National Institutes of Health Clinical Center from pre-nephrectomy healthy kidney donors or patients with neither major abdominal pathologies nor pancreatic cancer lesions. Segmentations of the pancreas are included with this data set; images were manually labeled slice-by-slice by a medical student, and verified/modified by an experienced radiologist. The BTCV data set comprises abdominal CT acquired at the Vanderbilt University Medical Center from metastatic liver cancer patients or post-operative ventral hernia patients. Segmentations of the spleen, right and left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland are included in this data set; images were manually labeled by two experienced undergraduate students, and verified by a radiologist on a volumetric basis using the MIPAV software.</p> <p>Segmentations that were not present in the original data sets were performed interactively using Matlab 2015b and ITK-SNAP 3.2 by an image research fellow under the supervision of a board-certified radiologist with 8 years of experience in gastrointestinal CT and MRI image interpretation. Segmentations that were present in the original data sets were edited to ensure a consistent segmentation protocol across the data set.</p> <p>Terms of use</p> <p>The terms of use of this data set include the terms of use of both the <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">TCIA Pancreas-CT data set</a> (see tabs for data links and terms of use) and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>). If you use these reference segmentations, please cite the above manuscript and the references below. Because these data include manual segmentations of images from the Beyond the Cranial Vault challenge test data, they may not be used to develop submissions for the challenge.</p> <p>References</p> <p>[1] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transactions on Medical Imaging, 2018.</p> <p>[2] Roth HR, Farag A, Turkbey EB, Lu L, Liu J, and Summers RM. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p> <p>[3] Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. <a href="http://arxiv.org/pdf/1506.06448.pdf">http://arxiv.org/pdf/1506.06448.pdf</a></p> <p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <a href="http://doi.org/10.1007/s10278-013-9622-7">http://doi.org/10.1007/s10278-013-9622-7</a></p> <p>[5] Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, and Landman BA, "Evaluation of six registration methods for the human abdomen on clinically acquired CT," IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1563–1572, 2016.<a href="http://doi.org/10.1109/TBME.2016.2574816">http://doi.org/10.1109/TBME.2016.2574816</a></p> <p>[6] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, <a href="https://doi.org/10.7303/syn3193805">https://doi.org/10.7303/syn3193805</a></p> <p>File format Labels are in NIfTI format with the following label definitions. Labels marked with * are only available in the BTCV data set.</p> <ol> <li>spleen</li> <li>right kidney*</li> <li>left kidney</li> <li>gallbladder</li> <li>esophagus</li> <li>liver</li> <li>stomach</li> <li>aorta*</li> <li>inferior vena cava*</li> <li>portal vein and splenic vein*</li> <li>pancreas</li> <li>right adrenal gland*</li> <li>left adrenal gland*</li> <li>duodenum</li> </ol> <p>Subjects included in the dataset</p> <p>The data comprises segmentation volumes for 90 cases, and the cropping coordinates (cropping.csv) used in the manuscript. The abdominal CT can be obtained from the links above. The reference standard segmentations may be incomplete outside of the specified cropping region. The cases are listed by their subject identifiers in their original data set:</p> <p> </p> <p><span class="math-tex">\(\begin{bmatrix} 1 & TCIA & Pancreas-CT & 0002\\ 2 & TCIA & Pancreas-CT & 0003\\ 3 & TCIA & Pancreas-CT & 0004\\ 4 & TCIA & Pancreas-CT & 0005\\ 5 & TCIA & Pancreas-CT & 0006\\ 6 & TCIA & Pancreas-CT & 0007\\ 7 & TCIA & Pancreas-CT & 0008\\ 8 & TCIA & Pancreas-CT & 0009\\ 9 & TCIA & Pancreas-CT & 0010\\ 10 & TCIA & Pancreas-CT & 0011\\ 11 & TCIA & Pancreas-CT & 0012\\ 12 & TCIA & Pancreas-CT & 0013\\ 13 & TCIA & Pancreas-CT & 0014\\ 14 & TCIA & Pancreas-CT & 0016\\ 15 & TCIA & Pancreas-CT & 0017\\ 16 & TCIA & Pancreas-CT & 0018\\ 17 & TCIA & Pancreas-CT & 0019\\ 18 & TCIA & Pancreas-CT & 0020\\ 19 & TCIA & Pancreas-CT & 0021\\ 20 & TCIA & Pancreas-CT & 0022\\ 21 & TCIA & Pancreas-CT & 0024\\ 22 & TCIA & Pancreas-CT & 0025\\ 23 & TCIA & Pancreas-CT & 0026\\ 24 & TCIA & Pancreas-CT & 0027\\ 25 & TCIA & Pancreas-CT & 0028\\ 26 & TCIA & Pancreas-CT & 0029\\ 27 & TCIA & Pancreas-CT & 0030\\ 28 & TCIA & Pancreas-CT & 0031\\ 29 & TCIA & Pancreas-CT & 0032\\ 30 & TCIA & Pancreas-CT & 0033\\ 31 & TCIA & Pancreas-CT & 0034\\ 32 & TCIA & Pancreas-CT & 0035\\ 33 & TCIA & Pancreas-CT & 0038\\ 34 & TCIA & Pancreas-CT & 0039\\ 35 & TCIA & Pancreas-CT & 0040\\ 36 & TCIA & Pancreas-CT & 0041\\ 37 & TCIA & Pancreas-CT & 0042\\ 38 & TCIA & Pancreas-CT & 0043\\ 39 & TCIA & Pancreas-CT & 0044\\ 40 & TCIA & Pancreas-CT & 0045\\ 41 & TCIA & Pancreas-CT & 0046\\ 42 & TCIA & Pancreas-CT & 0047\\ 43 & TCIA & Pancreas-CT & 0048\\ 44 & Synapse & BeyondTheCranialVault & 0001\\ 45 & Synapse & BeyondTheCranialVault & 0002\\ 46 & Synapse & BeyondTheCranialVault & 0003\\ 47 & Synapse & BeyondTheCranialVault & 0004\\ 48 & Synapse & BeyondTheCranialVault & 0005\\ 49 & Synapse & BeyondTheCranialVault & 0006\\ 50 & Synapse & BeyondTheCranialVault & 0007\\ 51 & Synapse & BeyondTheCranialVault & 0008\\ 52 & Synapse & BeyondTheCranialVault & 0009\\ 53 & Synapse & BeyondTheCranialVault & 0010\\ 54 & Synapse & BeyondTheCranialVault & 0021\\ 55 & Synapse & BeyondTheCranialVault & 0022\\ 56 & Synapse & BeyondTheCranialVault & 0023\\ 57 & Synapse & BeyondTheCranialVault & 0024\\ 58 & Synapse & BeyondTheCranialVault & 0025\\ 59 & Synapse & BeyondTheCranialVault & 0026\\ 60 & Synapse & BeyondTheCranialVault & 0027\\ 61 & Synapse & BeyondTheCranialVault & 0028\\ 62 & Synapse & BeyondTheCranialVault & 0029\\ 63 & Synapse & BeyondTheCranialVault & 0030\\ 64 & Synapse & BeyondTheCranialVault & 0031\\ 65 & Synapse & BeyondTheCranialVault & 0032\\ 66 & Synapse & BeyondTheCranialVault & 0033\\ 67 & Synapse & BeyondTheCranialVault & 0034\\ 68 & Synapse & BeyondTheCranialVault & 0035\\ 69 & Synapse & BeyondTheCranialVault & 0036\\ 70 & Synapse & BeyondTheCranialVault & 0037\\ 71 & Synapse & BeyondTheCranialVault & 0038\\ 72 & Synapse & BeyondTheCranialVault & 0039\\ 73 & Synapse & BeyondTheCranialVault & 0040\\ 74 & Synapse & BeyondTheCranialVault & 0061\\ 75 & Synapse & BeyondTheCranialVault & 0062\\ 76 & Synapse & BeyondTheCranialVault & 0063\\ 77 & Synapse & BeyondTheCranialVault & 0064\\ 78 & Synapse & BeyondTheCranialVault & 0065\\ 79 & Synapse & BeyondTheCranialVault & 0066\\ 80 & Synapse & BeyondTheCranialVault & 0067\\ 81 & Synapse & BeyondTheCranialVault & 0068\\ 82 & Synapse & BeyondTheCranialVault & 0069\\ 83 & Synapse & BeyondTheCranialVault & 0070\\ 84 & Synapse & BeyondTheCranialVault & 0074\\ 85 & Synapse & BeyondTheCranialVault & 0075\\ 86 & Synapse & BeyondTheCranialVault & 0076\\ 87 & Synapse & BeyondTheCranialVault & 0077\\ 88 & Synapse & BeyondTheCranialVault & 0078\\ 89 & Synapse & BeyondTheCranialVault & 0079\\ 90 & Synapse & BeyondTheCranialVault & 0080\\ \end{bmatrix}\)</span></p>
Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit
<p><span>Population of one million cases simulating a liver cancer scenario. The demographic information was taken from the USA 2019 Census Population Estimates by Age, Sex, Race, and Hispanic Origin</span><span>. The patient population was simulated in eight different cohorts with genders of male and female, and races/ethnicities of white, Black, Hispanic, and Asian. The total population sample size allowed the inclusion of a significant number of cases in each group. For each simulated patient, the age was randomly sampled from a uniform distribution, which spanned the age range of the individual demographic groups. Radiation risk, clinical risk, and total imaging procedure risk were also calculated.<br></span></p>
Merged Multi-Organ Abdominal CT Segmentation Dataset
<p><strong>Version 2.0</strong></p> <p>This version maintains the same dataset as Version 1.0, the only addition is the CSV metadata file. Please note that the BTCV images do not include metadata information.<br></p> <p> </p> <p><strong>Description</strong></p> <p>The abdominal CT images and reference segmentations were drawn from three datasets: the <a href="https://doi.org/10.7303/syn3193805" target="_blank" rel="noopener">Beyond the Cranial Vault (BTCV) Abdomen dataset</a> [1], the <a href="https://amos22.grand-challenge.org/" target="_blank" rel="noopener">Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset</a> [2], and the <a href="../records/10047292">TotalSegmentator dataset</a> [3]. Given the class differences among the three datasets, their consolidation requires the elimination of several classes, resulting in a unified dataset of 680 CT images comprising 12 classes common to all three, including:</p> <ul> <li>Spleen</li> <li>Right Kidney</li> <li>Left Kidney</li> <li>Gallbladder</li> <li>Esophagus</li> <li>Liver</li> <li>Stomach</li> <li>Aorta</li> <li>Inferior Vena Cava</li> <li>Pancreas</li> <li>Right Adrenal Gland</li> <li>Left Adrenal Gland</li> </ul> <p>The original work for which this dataset was created can be found in <a href="https://github.com/nicoalpis/Robust-Abdominal-Segmentation" target="_blank" rel="noopener">this GitHub repository</a>. </p> <p> </p> <p><strong>Terms of use</strong></p> <p>The terms of use of this data set include the terms of use of the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen dataset</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>), <a href="https://amos22.grand-challenge.org/" target="_blank" rel="noopener">Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset</a> (<a href="https://doi.org/10.5281/zenodo.7155725" target="_blank" rel="noopener">terms of use and data access</a>), and <a href="../records/10047292">TotalSegmentator dataset</a> (<a href="../records/10047292">terms of use and data access</a>). If you use these reference segmentations, please cite the references below.</p> <p><strong>References</strong></p> <p>[1] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, https://doi.org/10.7303/syn3193805.</p> <p>[2] Ji, Yuanfeng, et al. "Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation." Advances in Neural Information Processing Systems 35 (2022): 36722-36732.</p> <p>[3] Wasserthal, J., Breit, H. C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., ... & Segeroth, M. (2023). Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5).</p>
Micro-CT abdominal reconstruction of a worker honeybee abdomen
<p><strong>Micro-CT abdominal reconstruction of worker honeybees.</strong><em> </em></p> <p>The samples were obtained from a honeybee colony located in Cortijo Tinajas (Lecrín, Granada, Spain; coordinates: 36°55'25.1"N 3°31'42.9"W; 36.923645,-3.528575) and kindly provided by Apinevada S. L. Micro-CT scans were carried out at the Centro de Instrumentación Científica (University of Granada, Granada, UGR), using a Xradia 510 VERSA (ZEISS) (Xradia 510 VERSA (ZEISS). The following settings were established to get the same resolution of all samples: 4X magnification, 6.1515 μm pixel size, 200.021 mm mm source-sample distance, 20 mm detector-sample distance, and 2034 images.</p>
CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data
<p>This is the train and testing dataset of Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. This data consists of images of Abdominal CT and MRI from different patients.</p> <p>There are 20 training and 20 testing cases in the CT dataset. MRI dataset contains 20 training and 20 testing cases with T1-Dual and T2 SPIR sequences. Train data contains both DICOM images and their ground truth masks. The testing set only contains DICOM images. In CT cases only livers were annotated. In MRI cases, livers, left/right kidneys, and spleens were annotated. For further information about the data and challenge, please visit <a href="https://chaos.grand-challenge.org/">https://chaos.grand-challenge.org/</a> and read the <a href="https://www.dropbox.com/s/b8ka7tcxm45mlq4/CHAOS_Submission_Manual_new.pdf?dl=1">CHAOS_Submission_Manual.pdf</a></p> <p><em><strong>Important note: </strong>Ground truths/references of the test data are reserved for challenge validation and <strong>will never be shared publicly. </strong>Such requests will be ignored<strong>.</strong></em></p> <p>Scientists may use this data not only join to the CHAOS challenge but also for other works as long as they give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>Bibtex:</p> <pre><code>@dataset{CHAOSdata2019, author = {Ali Emre Kavur and M. Alper Selver and Oğuz Dicle and Mustafa Barış and N. Sinem Gezer}, title = {{CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data}}, month = Apr, year = 2019, publisher = {Zenodo}, version = {v1.03}, doi = {10.5281/zenodo.3362844}, url = {https://doi.org/10.5281/zenodo.3362844} }</code></pre> <p>IEEE Style:</p> <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, and N. Sinem Gezer, "CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data". Zenodo, 11-Apr-2019.</code></pre> <p>APA Style:</p> <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, & N. Sinem Gezer. (2019). CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3362844</code></pre> <p> </p>
Diagnostic Performance of Low-Dose CT for Acute Abdominal Conditions
ClinicalTrials.gov study NCT05651360. IPD Sharing: NO. Countries: 2. Publications: 24.
abnormal CT findings among patients with abdominal pain in the radiology department of a teriary care center
<p>These are Data were collected from the hospital system regarding demographic characteristics, past medical and surgical history, symptom description and physical examination findings, diagnosis (both initial and final), laboratory tests, CT findings, and results from other requested radiology modalities. All information was obtained after obtaining ethical approval.</p>
A Pan-cancer Screening and Diagnosis Model Based on Abdominal CT Was Established
ClinicalTrials.gov study NCT06614179. IPD Sharing: YES. Countries: 1. Publications: 1.
Developing a Decision Instrument to Guide Abdominal-pelvic CT Imaging of Blunt Trauma Patients
ClinicalTrials.gov study NCT04937868. IPD Sharing: YES. Countries: 1. Publications: 40.
TRACT Study: Evaluation of the Value of Routine Thoraco-abdominal CT in Blunt Trauma Patients
ClinicalTrials.gov study NCT00228111. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Pre-Operative Imaging of Abdominal Wall Perforators Using CT Angiography
ClinicalTrials.gov study NCT00748722. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Gastric Cancer Patients
ClinicalTrials.gov study NCT06606912. IPD Sharing: NO. Countries: 1. Publications: 3.
Preoperative Abdominal Enhanced CT, 18F-FDG PET/CT and 68Ga-FAPI PET/CT in Peritoneal Carcinomatosis of Gastric Cancer
ClinicalTrials.gov study NCT04778345. IPD Sharing: Not stated. Countries: 1. Publications: 14.
MRI as an Alternative to CT for Exploration of Acute Abdominal Pain in Young Women
ClinicalTrials.gov study NCT03281031. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Colorectal Cancer Patients
ClinicalTrials.gov study NCT06614699. IPD Sharing: NO. Countries: 1. Publications: 3.
Omnipaque Versus Gastroview as Oral Contrast for Abdominal and Pelvic CT
ClinicalTrials.gov study NCT00478556. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Abdominal Compartment Syndrome : Diagnostic and Prognostic Value of CT Findings - a Prospective Study
ClinicalTrials.gov study NCT02814734. IPD Sharing: NO. Countries: 0. Publications: 14.
Single Dose Safety, Tolerance and Pharmacokinetic Study in Healthy Volunteers Undergoing Contrast-enhanced Abdominal Computed Tomography (CT)
ClinicalTrials.gov study NCT01004770. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Limited Abdominal CT in ED Patients With Abdominal Pain
ClinicalTrials.gov study NCT02146521. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CT-based Model for Predicting Prolonged Weaning in Patients With Abdominal Trauma
ClinicalTrials.gov study NCT06339073. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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