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ShareScore release 0.9.0
Dataset results
50 results for “Abdominal Organ”
Organ Dysfunction Associated With Intra Abdominal Pressures in Liver Transplantation
ClinicalTrials.gov study NCT04463745. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Normal Vascular Doppler Pattern of Abdominal Organ in Children
ClinicalTrials.gov study NCT05290168. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Tulane Abdominal Transplant Institute (TATI) of Solid Organ Transplantation of HIV-Positive Recipients From HIV-Positive Donors
ClinicalTrials.gov study NCT05159466. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pain After Laparoscopy Results From the Incision, Manipulation of the Abdominal Organs, and Retained CO2(Back Pain)
ClinicalTrials.gov study NCT05186467. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of Moxonidine on Blood Pressure and Regression of Early Target Organ Damage in Young Subjects With Abdominal Obesity and Hypertension
ClinicalTrials.gov study NCT01360710. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Quantification of Abdominal Organ Motion Using MRI
ClinicalTrials.gov study NCT04748094. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Modeling Organ Motion During Abdominal Imaging in Healthy Volunteers
ClinicalTrials.gov study NCT05674851. IPD Sharing: NO. Countries: 1. Publications: 0.
Expanded Access for Use of ExoFlo in Abdominal Solid Organ Transplant Patients
ClinicalTrials.gov study NCT05215288. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@inproceedings{NEURIPS2022_ee604e1b, author = {Ji, Yuanfeng and Bai, Haotian and GE, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and Luo, Ping}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {36722--36732}, publisher = {Curran Associates, Inc.}, title = {AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ee604e1bedbd069d9fc9328b7b9584be-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} } </pre> </blockquote> <p> </p>
Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs
<p>Cressoni M, Cozzi A, Schiaffino S, Cadringher P, Vitali P, Basso G, Ippolito D, Sardanelli F. Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs. Eur Radiol Exp. 2022 Aug 29;6(1):37. doi: 10.1186/s41747-022-00292-y. PMID: 36031643; PMCID: PMC9420683.</p> <p>Abstract</p> <p><strong>Background: </strong>Computed tomography perfusion imaging (CTPI) by repeated scanning has clinical relevance but implies relatively high radiation exposure. We present a method to measure perfusion from two CT scan phases only, considering tissue enhancement, feeding vessel (aortic) peak enhancement, and bolus shape.</p> <p><strong>Methods: </strong>CTPI scans (each with 40 frames acquired every 1.5 s) of 11 patients with advanced hepatocellular carcinoma (HCC) enrolled between 2012 and 2016 were retrospectively analysed (aged 69 ± 9 years, 8/11 males). Perfusion was defined as the maximal slope of the time-enhancement curve divided by the peak enhancement of the feeding vessel (aorta). Perfusion was computed two times, first using the maximum slope derived from all data points and then using the peak tissue enhancement and the bolus shape obtained from the aortic curve.</p> <p><strong>Results: </strong>Perfusion values from the two methods were linearly related (r<sup>2</sup> = 0.92, p < 0.001; Bland-Altman analysis bias -0.12). The mathematical model showed that the perfusion ratio of two ROIs with the same feeding vessel (aorta) corresponds to their peak enhancement ratio (r<sup>2</sup> = 0.55, p < 0.001; Bland-Altman analysis bias -0.68). The relationship between perfusion and tissue enhancement is predicted to be linear in the clinical range of interest, being only function of perfusion, peak feeding vessel enhancement, and bolus shape.</p> <p><strong>Conclusions: </strong>This proof-of-concept study showed that perfusion values of HCC, kidney, and pancreas could be computed using enhancement measured only with two CT scan phases, if aortic peak enhancement and bolus shape are known.</p> <p> </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.