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217 results for “image contrast”

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zenodo44/100

Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism

<p>Datasets related to the research article &#39;Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism&#39;.&nbsp;<br> A readme with the necessary Matlab code to load the data is included.<br> Tested on Matlab2020b</p> <p>For the analytic rupture radiation simulation code please check the linked github repository.</p>

opencc-by-4.0Jul 2022View details →
zenodo44/100

Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"

<p>This dataset contains images that were used in the analysis of the manuscript &quot;Synchrotron-based phase contrast imaging of cardiovascular tissue in mice&mdash;grating interferometry or phase propagation?&quot;, that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>

opencc-by-4.0Apr 2019View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 2 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the second part of 14 parts of the full dataset (2/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each&nbsp; simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 3 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the third&nbsp;part of 14 parts of the full dataset (3/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 20ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each&nbsp; simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 30ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth&nbsp;part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 4 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourth part of 14 parts of the full dataset (4/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 25ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each of simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 11 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eleventh part of 14 parts of the full dataset (11/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 25ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 10 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the tenth part of 14 parts of the full dataset (10/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 20ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 14 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourteenth and final part of 14 parts of the full dataset (14/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 40ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 6 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the sixth part of 14 parts of the full dataset (6/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 8 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eighth part of 14 parts of the full dataset (8/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 10ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;600ms, 700ms,&nbsp; 800ms, and echo time (TE) = 35ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p>&nbsp;</p>

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Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14

<p>This dataset is part of the work&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the seventh part of 14 parts of the full dataset (7/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively&nbsp;repetition time (TR) =&nbsp;300ms, 400ms,&nbsp; 500ms, and echo time (TE) = 40ms. Under&nbsp;<strong>each</strong>&nbsp;simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb&nbsp;<a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data&nbsp;<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>&nbsp;and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at&nbsp;<a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>&nbsp;where the details of the data construction are discussed.</p> <p>All&nbsp;parts of the whole dataset can be found at:</p> <p>Part 1:&nbsp;<a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2:&nbsp;<a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3:&nbsp;<a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4:&nbsp;<a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5:&nbsp;<a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6:&nbsp;<a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7:&nbsp;<a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8:&nbsp;<a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9:&nbsp;<a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10:&nbsp;<a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11:&nbsp;<a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12:&nbsp;<a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13:&nbsp;<a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14:&nbsp;<a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>

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Dataset containing laser speckle-contrast images

<p>Dataset contains laser speckle-contrast images of human skin under various physiological tests (controlled respiration test, breath holding test, venous occlusion test).</p>

opencc-by-4.0Dec 2020View details →
zenodo40/100

Micro-CT image of cell-populated collagen scaffold in the aqueous environment (contrasted with PTA)

<p>A dataset of the collagen scaffold populated with the 3T3 cells scanned in the aqueous environment using Bruker Skyscan 1276 machine (Bruker, Belgium).&nbsp;</p><p><strong>Scaffold production</strong></p><p>The processes of obtaining and working with collagen scaffolds were conducted in an isolated environment under sterile conditions. The collagen sponge matrix was manufactured at the Center for Collagen Innovation within the Institute of Regenerative Medicine at Sechenov University and provided to us for experimental purposes. In order to obtain the collagen, the authors utilized animal-derived materials sourced from the tendons of large horned cattle. To do this, the tendons were cleaned of excess tissues, cut into pieces with a thickness of 0.5-1 cm, and sequentially treated for 12 hours in a 0.5 M NaCl solution. Subsequently, the mass was homogenized in a 0.83 M acetic acid solution. The resulting suspension was hydrolyzed with 0.24% pepsin for 2 days, after which 1 M NaOH was added to adjust the pH to 7.5, halting the hydrolysis process. The suspension was precipitated with a 12% NaCl solution, the resulting precipitate was redissolved in 0.02 M acetic acid, and then dialyzed. To obtain collagen porous matrices (sponges), the obtained solution was neutralized using 0.1 M NaOH until a pH of 7-7.5 was reached, and the resulting suspension was lyophilized at -40°C for 2 days.</p><p>Subsequently, the collagen matrix was cut into cubes with sides measuring 0.5 cm. These cubes were placed in 15 ml test tubes filled with 70% ethyl alcohol for sterilization. The test tubes were then placed on a shaker and left in the refrigerator at +4°C for 24 hours. Afterward, the collagen matrices were removed from the alcohol and rinsed five times with 0.9% NaCl.</p><p>Following the alcohol rinse to confirm the absence of toxicity, an elution test, adapted following the ISO 10993 protocol, was conducted. To obtain collagen cube extracts, they were incubated in a cell culture medium at a volume of 1 ml per sample for 24 hours at 37°C. The 3T3 cell culture was passaged, with 5000 cells seeded in each well of a 96-well plate. After 24 hours, the cells were treated with extract at a volume of 200 µl per well and left in the incubator at 37°C for 24 hours. The following day, extracts were collected, and AlamarBlue reagent (Invitrogen, Waltham, MA, USA) was added according to the manufacturer's instructions to assess the metabolic activity of the cells. Serial dilutions of sodium dodecyl sulfate (SDS) were used as the positive control. Fluorescence intensity was measured using a Victor Nivo spectrofluorimeter (PerkinElmer, Waltham, Massachusetts, USA) at an excitation wavelength of 530 nm and an emission wavelength of 590 nm.</p><p><strong>Cell seeding</strong></p><p>After confirming the absence of cytotoxic effects, collagen sponges were seeded with the NIH 3T3 cell line at a density of 50,000 cells per sample (cubes of collagen sponge measuring 0.5 cm per side).&nbsp;</p><p><strong>Staining technique</strong></p><p>Fixed specimens in 10% formalin with PBS were washed after 24 hours with distilled water and after that placed in 3% phosphotungstic acid dissolved in distilled water for 24 hours and kept on the rotary shaker at room temperature. After staining, samples were washed and stored in distilled water at 5 °C.&nbsp;</p><p><strong>Image acquisition and reconstruction</strong></p><p>A plastic tube filled with distilled water containing the contrasted sample was placed on the sample holder in a SkyScan 1276 micro-CT (Bruker, Kontich, Belgium) and were scanned at 3 μm voxel resolution with 70 kV voltage and 200 uA source power and an aluminum filter with 1 mm of thickness. The rotation was set to 360° around the vertical axis of the sample, with two middle frames for each 0.2° angle step.</p><p>After scanning, the data were reconstructed using Bruker's NRecon software. During reconstruction, the ring artifact reduction value was set to 20% and the beam hardening correction value to 30%.&nbsp;After that, samples were exported as a series of 16-bit TIFF images which could be opened in the specialized software.&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo40/100

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>&nbsp;</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>&nbsp;</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>&lsquo;manualGT_rater1.stl&rsquo; &ndash; 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>&nbsp;A sample belonging to the <em>test-GT</em> set (Bujny et al. 2024).</li> <li>&lsquo;manualGT_rater2.stl&rsquo; &ndash; corresponding <em>test-GT</em> sample by the second rater.</li> <li>&lsquo;ML.stl&rsquo; &ndash; post-processed inference of the nnU-Net ML model in the STL format.</li> <li>&lsquo;report.html&rsquo; &ndash; 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>

opencc-by-4.0May 2023View details →
zenodo40/100

Cellpose model for Digital Phase Contrast images

<p><strong>Name: </strong>Cellpose model for Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose model, trained via transfer learning from &lsquo;cyto&rsquo; model.</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>)</p> <p><strong>Training Procedure: </strong>Model was trained using a&nbsp;Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a>&nbsp;</p> <p>python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0</p> <p>The model file (MODEL NAME) in this repository is the result of this training.</p> <p><strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with</p> <p>python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images

<p><strong>Name:&nbsp;</strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type:&nbsp;</strong>Cellpose models trained via transfer learning from the &lsquo;nuclei&rsquo; and &lsquo;cyto2&rsquo; pretrained model with additional <strong>Training Dataset . Includes</strong>&nbsp;corresponding&nbsp;csv files with &#39;Quality Control&#39; metrics(&sect;) (model.zip).</p> <p><strong>Training Dataset:&nbsp;</strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the&nbsp;<a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a>&nbsp;. Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p>&nbsp;</p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei  --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p>&nbsp;</p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei  --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p>&nbsp;</p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p>&nbsp;</p> <p><em><strong>NOTE&nbsp;</strong></em>(&sect;):&nbsp;We provide&nbsp;a notebook for Quality Control, which is an adaptation of the&nbsp;<a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">&quot;Cellpose (2D and 3D)&quot; notebook from ZeroCostDL4Mic</a>&nbsp;.</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the &ldquo;HeLa &ldquo;Kyoto&rdquo; cells&nbsp;under the scope&rdquo; &nbsp;dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the &ldquo;_flow&rdquo; images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p>&nbsp;</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells

<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37&deg;C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 &micro;m/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 &micro;m/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment &amp; Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) &ndash; (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment &amp; Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same &ldquo;Donor_Timepoint_Treatment/Well...&rdquo; (i.e. U13_24_C1&hellip;). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same &ldquo;Donor_Timepoint_Treatment/Well_FieldofView&hellip;&rdquo; (i.e. U13_24_C1_1&hellip;).</p>

opencc-by-4.0Aug 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record