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Medical Out-of-Distribution Analysis Challenge [article]

David Zimmerer, Jens Petersen, Gregor Köhler, Paul Jäger, Peter Full, Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein, Klaus Maier-Hein
<span title="2020-03-19">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3784230">doi:10.5281/zenodo.3784230</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ftxqospoxbgyzfnutehxsh4voa">fatcat:ftxqospoxbgyzfnutehxsh4voa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505024009/https://zenodo.org/record/3784230/files/MedicalOut-of-DistributionAnalysisChallenge_v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/80/d0/80d02295d8642315162071d873afa948fbcb8788.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3784230"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Medical Out-of-Distribution Analysis Challenge [article]

Jens Petersen, Gregor Köhler, Paul Jäger, Peter Full, David Zimmerer, Klaus Maier-Hein, Tobias Roß, Tim Adler, Annika Reinke, Lena Maie-Hein
<span title="2020-03-19">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ...  Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3715870">doi:10.5281/zenodo.3715870</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ozjhttwqrjac5axidc2olbnptq">fatcat:ozjhttwqrjac5axidc2olbnptq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320180044/https://zenodo.org/record/3715870/files/MedicalOutOfDistributionAnalysisChallenge.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3715870"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Medical Out-of-Distribution Analysis Challenge [article]

David Zimmerer, Jens Petersen, Gregor Köhler, Paul Jäger, Peter Full, Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein, Klaus Maier-Hein
<span title="2020-03-19">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3961376">doi:10.5281/zenodo.3961376</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/haipimo7d5hhtne2qpdnfmiibm">fatcat:haipimo7d5hhtne2qpdnfmiibm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200807205756/https://zenodo.org/record/3961376/files/MedicalOut-of-DistributionAnalysisChallenge_v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3961376"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Medical Out-of-Distribution Analysis Challenge 2021 [article]

Jens Petersen, Gregor Köhler, Paul Jäger, Peter Full, David Zimmerer, Klaus Maier-Hein, Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein
<span title="2021-03-02">2021</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ...  Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4573947">doi:10.5281/zenodo.4573947</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/psj5lxronbg75ohbphrywrgwkm">fatcat:psj5lxronbg75ohbphrywrgwkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428181529/https://zenodo.org/record/4573948/files/MedicalOut-of-DistributionAnalysisChallenge2021_02-11-2021_10-28-29.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/49/36/4936f32111ac3ad258d2af24277244168f99f981.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4573947"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Medical Out-of-Distribution Analysis Challenge 2022 [article]

David Zimmerer, Jens Petersen, Gregor Köhler, Paul Jäger, Peter Full, Klaus Maier-Hein, Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein
<span title="2022-03-16">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ...  Medical Image Computing (MIC), German Cancer Research Center (DKFZ) Tobias Roß, Tim Adler, Annika Reinke, Lena Maier-Hein Div Computer Assisted Medical Interventions (CAMI), German Cancer Research Center  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6362312">doi:10.5281/zenodo.6362312</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/atuagbat6ze3xbnzqfkot434ea">fatcat:atuagbat6ze3xbnzqfkot434ea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505200536/https://zenodo.org/record/6362313/files/MedicalOut-of-DistributionAnalysisChallenge2022_03-16-2022_10-24-22.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/d3/4ed3656924f6543748f1e2a56774ec4ce41bd094.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6362312"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Lipid mediator profile in vernix caseosa reflects skin barrier development

Antonio Checa, Tina Holm, Marcus O. D. Sjödin, Stacey N. Reinke, Johan Alm, Annika Scheynius, Craig E. Wheelock
<span title="2015-11-02">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
Vernix caseosa (VC) is a protective layer that covers the skin of most human newborns. This study characterized the VC lipid mediator profile, and examined its relationship to gestational period, gender of the newborn and maternal lifestyle. VC collected at birth from 156 newborns within the ALADDIN birth cohort was analyzed and 3 different groups of lipid mediators (eicosanoids and related oxylipin analogs, endocannabinoids and sphingolipids) were screened using LC-MS/MS. A total of 54
more &raquo; ... s were detected in VC. A number of associations between lipid mediators and the gestational period were observed, including increases in the ceramide to sphingomyelin ratio as well as the endocannabinoids anandamide and 2-arachidonoylglycerol. Gender-specific differences in lipid mediator levels were observed for all 3 lipid classes. In addition, levels of the linoleic acid oxidation products 9(10)-epoxy-12Z-octadecenoic and 12(13)-epoxy-9Z-octadecenoic acid (EpOMEs) as well as 12,13-dihydroxy-9Z-octadecenoic acid (DiHOME) were increased in VC of children from mothers with an anthroposophic lifestyle. Accordingly, VC was found to be rich in multiple classes of bioactive lipid mediators, which evidence lifestyle, gender and gestational week dependencies. Levels of lipid mediators in VC may therefore be useful as early stage non-invasive markers of the development of the skin as a protective barrier.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep15740">doi:10.1038/srep15740</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26521946">pmid:26521946</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4629127/">pmcid:PMC4629127</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qy3ntljq4nco7gxdplbw4l4ypa">fatcat:qy3ntljq4nco7gxdplbw4l4ypa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200313221959/https://www.nature.com/articles/srep15740.pdf?error=cookies_not_supported&amp;code=48841e8c-2030-433c-a85e-f1a658209a40" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b0/fe/b0fe6921d33aa032dfce2b4dd48746d74f5c42d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep15740"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4629127" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Biomedical Image Analysis Challenges (BIAS) Reporting Guideline [article]

Lena Maier-Hein, Annika Reinke, Michal Kozubek, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbury, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken (+2 others)
<span title="2020-08-31">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control),
more &raquo; ... the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. We present a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4008954">doi:10.5281/zenodo.4008954</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5ub5eddlxvdxdawmab3jiikyuq">fatcat:5ub5eddlxvdxdawmab3jiikyuq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902154526/https://zenodo.org/record/4008954/files/AppendixAReportingGuideline.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dd/e3/dde345eab323b930da60557ef48e777a1c80f5c0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4008954"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Snke OS 3D Lung CT Segmentation Challenge [article]

Simon Weidert, Seyed-Ahmad Ahmadi, Fausto Milletarì, Linda Bij De Leij, Stefan Tain, Jens Elsner, Thomas Heiliger, Felix Swamy V. Zastrow, Wolfgang Männel, Szilard Szabo, Gergely Dietz, Pál Maurovich Horvat (+4 others)
<span title="2020-08-20">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
This is the structured challenge design document for the "Snke OS 3D Lung CT Segmentation Challenge". More details can be found on the challenge's website. The structured design was introduced by the Biomedical Image Analysis ChallengeS (BIAS) initiative. Background: Since the outbreak of the global Covid19 pandemic, the number of confirmed COVID-19 cases has reached over 16 million globally [1, 2], affecting virtually every territory, and with a fatality rate ~2-3% among the cohort of
more &raquo; ... ive cases. Given the high demand for effective diagnosis and treatment of cases, the WHO recently released a rapid advice guide in July 2020 [3], in which chest imaging is conditionally recommended for several purposes, e.g. to aid diagnosis in the absence/delay of PCR testing, to assess the need for ICU admission and to inform the therapeutic management of patients. Purpose: In this challenge, we aim to aid radiologists and physicians through objective and quantitative computational assessment of chest imaging in the context of COVID-19. We provide access to a large dataset of 3D chest CT imaging of the lung, collected from several European and international radiological centers. We call the international research community to develop and test artificial intelligence algorithms on this dataset. Dataset: We provide access to low-dose chest CT imaging volumes from a mixed cohort of COVID-19 and non- COVID-19 cases. The dataset contains 113 labeled/segmented cases (79 COVID-19, 34 non-COVID-19), and >100 unlabeled volumes. A particular scientific challenge will lie in the effective use of unlabeled data through semi- and self-supervised training techniques. Labels represent five lung lobes and two lesions types, consolidation and ground-glass opacities. Labels are provided in a multi-hot encoding to allow region overlaps (e.g. lesions within lung lobes). For local development, we provide a realistic [...]
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4010165">doi:10.5281/zenodo.4010165</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/grhbvnmon5ex7madxr7d7iqjbu">fatcat:grhbvnmon5ex7madxr7d7iqjbu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200903040405/https://zenodo.org/record/4010165/files/SnkeOS3DLungCTSegmentationChallenge_v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4010165"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Ten years of image analysis and machine learning competitions in dementia [article]

Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby
<span title="2022-02-18">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade. The seven grand challenges addressed questions
more &raquo; ... related to screening, clinical status estimation, prediction and monitoring in (pre-clinical) dementia. There was little overlap in clinical questions, tasks and performance metrics. Whereas this aids providing insight on a broad range of questions, it also limits the validation of results across challenges. The validation process itself was mostly comparable between challenges, using similar methods for ensuring objective comparison, uncertainty estimation and statistical testing. In general, winning algorithms performed rigorous data preprocessing and combined a wide range of input features. Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used. To increase impact, future challenges could pay more attention to statistical analysis of which factors relate to higher performance, to clinical questions beyond Alzheimer's disease, and to using testing data beyond the Alzheimer's Disease Neuroimaging Initiative. Grand challenges would be an ideal venue for assessing the generalizability of algorithm performance to unseen data of other cohorts. Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07922v2">arXiv:2112.07922v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tfexsjejife3vav4ywx3nmifdy">fatcat:tfexsjejife3vav4ywx3nmifdy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211225212329/https://arxiv.org/pdf/2112.07922v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/39/5739ec3b97511fb999ab6afc3b18bf586f1f5634.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07922v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Methods and open-source toolkit for analyzing and visualizing challenge results

Manuel Wiesenfarth, Annika Reinke, Bennett A. Landman, Matthias Eisenmann, Laura Aguilera Saiz, M. Jorge Cardoso, Lena Maier-Hein, Annette Kopp-Schneider
<span title="2021-01-27">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
AbstractGrand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of
more &raquo; ... this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-021-82017-6">doi:10.1038/s41598-021-82017-6</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33504883">pmid:33504883</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tbxbq6kbknbsjigizxvqdeufhu">fatcat:tbxbq6kbknbsjigizxvqdeufhu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429001441/https://www.nature.com/articles/s41598-021-82017-6.pdf?error=cookies_not_supported&amp;code=db168f61-1476-4a8d-970c-6ac70ab2c460" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/b6/20b63628e90e7b0d1d8bb769edc8f82a2c192a64.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-021-82017-6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Methods and open-source toolkit for analyzing and visualizing challenge results [article]

Manuel Wiesenfarth, Annika Reinke, Bennett A. Landman, Manuel Jorge Cardoso, Lena Maier-Hein, Annette Kopp-Schneider
<span title="2019-12-05">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Reinke and L.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.05121v2">arXiv:1910.05121v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5vovwwfq3vcztchcgavtiadiyy">fatcat:5vovwwfq3vcztchcgavtiadiyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200925114533/https://arxiv.org/pdf/1910.05121v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/95/9d/959d3158e9e28c066134f7c333a6972f62ae0c73.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.05121v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Snke OS 3D Lung CT Segmentation Challenge [article]

Simon Weidert, Seyed-Ahmad Ahmadi, Fausto Milletarì, Linda Bij De Leij, Stefan Tain, Jens Elsner, Thomas Heiliger, Felix Swamy V. Zastrow, Wolfgang Männel, Szilard Szabo, Gergely Dietz, Pál Maurovich Horvat (+4 others)
<span title="2020-08-20">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
This is the structured challenge design document for the "Snke OS 3D Lung CT Segmentation Challenge". More details can be found on the challenge's website. The structured design was introduced by the Biomedical Image Analysis ChallengeS (BIAS) initiative. Background: Since the outbreak of the global Covid19 pandemic, the number of confirmed COVID-19 cases has reached over 16 million globally [1, 2], affecting virtually every territory, and with a fatality rate ~2-3% among the cohort of
more &raquo; ... ive cases. Given the high demand for effective diagnosis and treatment of cases, the WHO recently released a rapid advice guide in July 2020 [3], in which chest imaging is conditionally recommended for several purposes, e.g. to aid diagnosis in the absence/delay of PCR testing, to assess the need for ICU admission and to inform the therapeutic management of patients. Purpose: In this challenge, we aim to aid radiologists and physicians through objective and quantitative computational assessment of chest imaging in the context of COVID-19. We provide access to a large dataset of 3D chest CT imaging of the lung, collected from several European and international radiological centers. We call the international research community to develop and test artificial intelligence algorithms on this dataset. Dataset: We provide access to low-dose chest CT imaging volumes from a mixed cohort of COVID-19 and non- COVID-19 cases. The dataset contains 113 labeled/segmented cases (79 COVID-19, 34 non-COVID-19), and >100 unlabeled volumes. A particular scientific challenge will lie in the effective use of unlabeled data through semi- and self-supervised training techniques. Labels represent five lung lobes and two lesions types, consolidation and ground-glass opacities. Labels are provided in a multi-hot encoding to allow region overlaps (e.g. lesions within lung lobes). For local development, we provide a realistic [...]
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The Federated Tumor Segmentation (FeTS) Challenge 2022 [article]

Spyridon Bakas, Sarthak Pati, Micah Sheller, Alexandros Karargyris, Peter Mattson, Brandon Edwards, Ujjwal Baid, Yong Chen, Russell (Taki) Shinohara, Jason Martin, Bjoern Menze, Maximilian Zenk (+6 others)
<span title="2022-03-16">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on "real-world" clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and
more &raquo; ... nership hurdles. Building upon the Federated Tumor Segmentation (FeTS) 2021 challenge, which represents the first challenge to ever be proposed on federated learning, FeTS 2022 intends to address these hurdles, both for the creation and the evaluation of tumor segmentation models. Specifically, the FeTS 2022 challenge will use clinically acquired, multiinstitutional multi-parametric magnetic resonance imaging (mpMRI) scans from the RSNA-ASNR-MICCAI BraTS 2021 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (www.fets.ai). The FeTS 2022 challenge focuses on the construction and evaluation of a consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas (and particularly the radiographically appearing glioblastomas). Compared to the BraTS challenge [1-4], the ultimate goal of FeTS is 1) the creation of a consensus segmentation model that has gained knowledge from data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution), and 2) the valuation of segmentation models in such a federated configuration (i.e., in the wild). The FeTS 2022 challenge is structured in two tasks: Task 1 ("Federated Training") aims at effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task [...]
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The Federated Tumor Segmentation (FeTS) Challenge 2022 [article]

Spyridon Bakas, Sarthak Pati, Micah Sheller, Alexandros Karargyris, Peter Mattson, Brandon Edwards, Ujjwal Baid, Yong Chen, Russell (Taki) Shinohara, Jason Martin, Bjoern Menze, Maximilian Zenk (+6 others)
<span title="2022-03-16">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on "real-world" clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and
more &raquo; ... nership hurdles. Building upon the Federated Tumor Segmentation (FeTS) 2021 challenge, which represents the first challenge to ever be proposed on federated learning, FeTS 2022 intends to address these hurdles, both for the creation and the evaluation of tumor segmentation models. Specifically, the FeTS 2022 challenge will use clinically acquired, multiinstitutional multi-parametric magnetic resonance imaging (mpMRI) scans from the RSNA-ASNR-MICCAI BraTS 2021 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (www.fets.ai). The FeTS 2022 challenge focuses on the construction and evaluation of a consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas (and particularly the radiographically appearing glioblastomas). Compared to the BraTS challenge [1-4], the ultimate goal of FeTS is 1) the creation of a consensus segmentation model that has gained knowledge from data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution), and 2) the valuation of segmentation models in such a federated configuration (i.e., in the wild). The FeTS 2022 challenge is structured in two tasks: Task 1 ("Federated Training") aims at effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task [...]
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Ultrasonographic Validation for Needle Placement in the Tibialis Posterior Muscle

Stephanie R. Albin, Larisa R. Hoffman, Cameron W. MacDonald, Micah Boriack, Lauren Heyn, Kaleb Schuler, Annika Taylor, Jennie Walker, Shane L. Koppenhaver, Mark F. Reinking
<span title="2021-12-02">2021</span> <i title="International Journal of Sports Physical Therapy"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l6dxfein2vdbdjj7qwdo32yh5y" style="color: black;">International Journal of Sports Physical Therapy</a> </i> &nbsp;
The tibialis posterior (TP) muscle plays an important role in normal foot function. Safe, efficacious therapeutic approaches addressing this muscle are necessary; however, the location of the muscle in the deep posterior compartment can create challenges. The purpose of this study was to assess the accuracy of needle placement in the TP muscle and determine the needle placement in relation to the neurovascular structures located within the deep compartment. Cross Sectional Study. Needle
more &raquo; ... t and ultrasound imaging were performed on 20 healthy individuals. A 50 mm or 60 mm needle was inserted between 30 - 50% of the tibial length measured from the medial tibiofemoral joint. The needle was inserted in a medial to lateral direction into the right extremity with the patient in right side lying. Placement of the needle into the TP muscle was verified with ultrasound imaging, and the shortest distance from the needle to the posterior tibial artery and tibial nerve was measured. The depth from the skin to the superficial border of the TP muscle was also measured. Ultrasonography confirmed the needle filament was inserted into the TP muscle in all 20 individuals and did not penetrate the neurovascular bundle in any individual. The mean distance from the needle to the tibial nerve and posterior tibial artery was 10.0 + 4.7 mm and 10.2 + 4.7 mm respectively. The superficial border of the TP muscle from the skin was at a mean depth of 25.8 + 4.9 mm. This ultrasound imaging needle placement study supports placement of a solid filament needle into the TP muscle with avoidance of the neurovascular structures of the deep posterior compartment when placed from a medial to lateral direction at 30-50% of the tibial length. 2b.
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