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Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data

Nature communications 2021年第1期12卷 4103页

作者： Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. Faculty of Biosciences Heidelberg University Heidelberg Germany. Division of Computational Genomics and System Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science Boston University Boston MA USA. Allen Institute for Brain Science Seattle WA USA. Division of molecular neurobiology Department of medical biochemistry and biophysics Karolinska Institutet Stockholm Sweden. Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany. Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital Heidelberg Germany. roland.eils@bih-charite.de. Berlin Institute of Health at Charité-Universitätsmedizin Berlin Digital Health Center Kapelle-Ufer 2 10117 Berlin Germany. naveed.ishaque@bih-charite.de.

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iLCM - A virtual research infrastructure for large-scale qualitative data

arXiv

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arXiv 2018年

作者： Niekler, Andreas Bleier, Arnim Kahmann, Christian Posch, Lisa Wiedemann, Gregor Erdogan, Kenan Heyer, Gerhard Strohmaier, Markus Faculty of Mathematics and Computer Science University Leipzig Augustusplatz 10 Leipzig04109 Germany Department of Computational Social Science GESIS Leibniz Institute for the Social Sciences Unter Sachsenhausen 6-8 Cologne50667 Germany Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16c Graz8010 Austria HumTec Institute RWTH Aachen University Theaterplatz 14 Aachen52062 Germany

The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS). The research environment addresses requirements for the quantitative evaluation of large amounts of qualitative data with text mining methods as well as requirements for the reproducibility of data-driven research designs in the social sciences. For this, the iLCM research environment comprises two central components. First, the Leipzig Corpus Miner (LCM), a decentralized SaaS application for the analysis of large amounts of news texts developed in a previous Digital Humanities project. Second, the text mining tools implemented in the LCM are extended by an "Open Research Computing" (ORC) environment for executable script documents, so-called "notebooks". This novel integration allows to combine generic, high-performance methods to process large amounts of unstructured text data and with individual program scripts to address specific research requirements in computational social science and digital humanities. ***. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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The first Hadamard variation of Neumann-Poincaré eigenvalues on the sphere

arXiv

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arXiv 2018年

作者： Ando, Kazunori Kang, Hyeonbae Miyanishi, Yoshihisa Ushikoshi, Erika Department of Electrical and Electronic Engineering and Computer Science Ehime University Ehime790-8577 Japan Department of Mathematics Institute of Applied Mathematics Inha University Incheon22212 Korea Republic of Center for Mathematical Modeling and Data Science Osaka University Osaka560-8531 Japan Faculty of Environment and Information Sciences Yokohama National University Kanagawa240-8501 Japan

The Neumann-Poincaré operator on the sphere has 2\(2k1+1\), k = 0, 1, 2,..., as its eigenvalues and the corresponding multiplicity is 2k + 1. We consider the bifurcation of eigenvalues under deformation of domains, and show that Frechét derivative of the sum of the bifurcations is zero. We then discuss the connection of this result with some conjectures regarding the Neumann-Poincaré *** Codes 47A45, 31B25 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Search for the doubly charmed baryon ■

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science China(Physics,Mechanics & Astronomy) 2020年第2期63卷 8-22页

作者： R.Aaij C.Abellán Beteta T.Ackernley B.Adeva M.Adinolfi H.Afsharnia C.A.Aidala S.Aiola Z.Ajaltouni S.Akar P.Albicocco J.Albrecht F.Alessio M.Alexander A.Alfonso Albero G.Alkhazov P.Alvarez Cartelle A.A.Alves Jr S.Amato Y.Amhis L.An L.Anderlini G.Andreassi M.Andreotti F.Archilli J.Arnau Romeu A.Artamonov M.Artuso K.Arzymatov E.Aslanides M.Atzeni B.Audurier S.Bachmann J.J.Back S.Baker V.Balagura W.Baldini A.Baranov R.J.Barlow S.Barsuk W.Barter M.Bartolini F.Baryshnikov G.Bassi V.Batozskaya B.Batsukh A.Battig V.Battista A.Bay M.Becker F.Bedeschi I.Bediaga A.Beiter L.J.Bel V.Belavin S.Belin N.Beliy V.Bellee K.Belous I.Belyaev G.Bencivenni E.Ben-Haim S.Benson S.Beranek A.Berezhnoy *** D.Berninghoff H.C.Bernstein E.Bertholet A.Bertolin C.Betancourt F.Betti M.O.Bettler Ia.Bezshyiko S.Bhasin J.Bhom M.S.Bieker S.Bifani P.Billoir A.Birnkraut A.Bizzeti M.Bjφrn M.P.Blago T.Blake F.Blanc S.Blusk D.Bobulska V.Bocci O.Boente Garcia T.Boettcher A.Boldyrev A.Bondar N.Bondar S.Borghi M.Borisyak M.Borsato J.T.Borsuk T.J.V.Bowcock C.Bozzi S.Braun A.Brea Rodriguez M.Brodski J.Brodzicka A.Brossa Gonzalo D.Brundu E.Buchanan A.Buonaura C.Burr A.Bursche J.S.Butter J.Buytaert W.Byczynski S.Cadeddu H.Cai R.Calabrese S.Cali R.Calladine M.Calvi M.Calvo Gomez A.Camboni P.Campana D.H.Campora Perez L.Capriotti A.Carbone G.Carboni R.Cardinale A.Cardini P.Carniti K.Carvalho Akiba A.Casais Vidal G.Casse M.Cattaneo G.Cavallero R.Cenci J.Cerasoli M.G.Chapman M.Charles Ph.Charpentier G.Chatzikonstantinidis M.Chefdeville V.Chekalina C.Chen S.Chen A.Chernov S.-G.Chitic V.Chobanova M.Chrzaszcz A.Chubykin P.Ciambrone M.F.Cicala X.Cid Vidal G.Ciezarek F.Cindolo P.E.L.Clarke M.Clemencic H.V.Cliff J.Closier J.L.Cobbledick V.Coco J.A.B.Coelho J.Cogan E.Cogneras L.Cojocariu P.Collins T.Colombo A.Comerma-Montells A.Contu N.Cooke G.Coombs S.Coquereau G.Corti C.M.Costa Sobral B.Couturier D.C.Craik J.Crkovska A.Crocombe M.Cruz Torres R.Currie C.L.Da Silva E.Dall'Occo J.Dalseno C.D'Ambrosio A.Danilina P.d'Argent A.Davis O.De Aguiar Francisco K.De Bruyn S.De Capua M Centro Brasileiro de Pesquisas Fisicas(CBPF) Rio de JaneiroBrazil Universidade Federal do Rio de Janeiro(UFRJ) Rio de JaneiroBrazil Center for High Energy Physics Tsinghua UniversityBeijingChina School of Physics State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijingChina University of Chinese Academy of Sciences BeijingChina Institute Of High Energy Physics(IHEP) BeijingChina Institute of Particle Physics Central China Normal UniversityWuhanHubeiChina Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIN2P3-LAPPAnnecyFrance Universite Clermont Auvergne CNRS/IN2P3LPCClermont-FerrandFrance Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance LAL Univ.Paris-SudCNRS/IN2P3Universite Paris-SaclayOrsayFrance LPNHE Sorbonne Universite Paris Diderot Sorbonne Paris Cite CNRS/IN2P3ParisFrance I.Physikalisches Institut RWTH Aachen UniversityAachenGermany Fakultat Physik Technische Universitat DortmundDortmundGermany Max-Planck-Institut für Kernphysik(MPIK) HeidelbergGermany Physikalisches Institut Ruprecht-Karls-Universitat HeidelbergHeidelbergGermany School of Physics University College DublinDublinIreland INFN Sezione di Bari BariItaly INFN Sezione di Bologna BolognaItaly INFN Sezione di Ferrara FerraraItaly INFN Sezione di Firenze FirenzeItaly INFN Laboratori Nazionali di Frascati FrascatiItaly INFN Sezione di Genova GenovaItaly INFN Sezione di Milano-Bicocca MilanoItaly INFN Sezione di Milano MilanoItaly INFN Sezione di Cagliari MonserratoItaly INFN Sezione di Padova PadovaItaly INFN Sezione di Pisa PisaItaly INFN Sezione di Roma Tor Vergata RomaItaly INFN Sezione di Roma La Sapienza RomaItaly Nikhef National Institute for Subatomic Physics AmsterdamNetherlands Nikhef National Institute for Subatomic Physics and VU University Amsterdam AmsterdamNetherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences KrakowPoland AGH-University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakowPoland Natio

A search for the doubly charmed baryon ■^+cc is performed through its decay to theΛ^+c K^-π^+ final state,using proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7,8 and 13 *** data correspond to a total integrated luminosity of 9 fb^-*** significant signal is observed in the mass range from 3.4 to 3.8 GeV/c^*** limits are set at 95%credibility level on the ratio of the ■^+cc production cross-section times the branching fraction to that ofΛ^+c and ■^++cc *** limits are determined as functions of the ■^+cc mass for different lifetime hypotheses,in the rapidity range from 2.0 to 4.5 and the transverse momentum range from 4 to 15 GeV/c.

关键词： charmed baryons limits on production of particles charmed quarks experimental tests

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Allen: A high level trigger on GPUs for LHCb

arXiv

引用

arXiv 2019年

作者： Aaij, R. Albrecht, J. Belous, M. Billoir, P. Boettcher, T. Rodriguez, A. Brea vom Bruch, D. Campora Perez, D.H. Casais Vidal, A. Craik, D.C. Fernandez Declara, P. Funke, L. Gligorov, V.V. Jashal, B. Kazeev, N. Martinez Santos, D. Pisani, F. Pliushchenko, D. Popov, S. Quagliani, R. Rangel, M. Reiss, F. Mayordomo, C. Sanchez Schwemmer, R. Sokoloff, M. Stevens, H. Ustyuzhanin, A. Cardona, X. Vilasis Williams, M. Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Fakultat Physik Technische Universitat Dortmund Dortmund Germany National Research University Higher School of Economics Moscow Russia Yandex School of Data Analysis Moscow Russia LPNHE Sorbonne Universite Paris Diderot Sorbonne Paris Cite CNRS IN2P3 Paris France Massachusetts Institute of Technology Cambridge United States Universidade de Santiago de Compostela Santiago de Compostela Spain Faculty of Science and Engineering Maastricht University Maastricht Netherlands Geneva Switzerland Department of Computer Science and Engineering University Carlos III of Madrid Madrid Spain Instituto de Fisica Corpuscular Centro Mixto Universidad de Valencia CSIC Valencia Spain INFN Sezione di Bologna Bologna Italy

We describe a fully GPU-based implemen-tation of the first level trigger for the upgrade of the LHCb detector, due to start data taking in 2021. We demonstrate that our implementation, named Allen, can process the 40 Tbit/s data rate of the upgraded LHCb detector and perform a wide variety of pattern recognition tasks. These include finding the trajecto-ries of charged particles, finding proton-proton collision points, identifying particles as hadrons or muons, and finding the displaced decay vertices of long-lived par-ticles. We further demonstrate that Allen can be im-plemented in around 500 scientific or consumer GPU cards, that it is not I/O bound, and can be operated at the full LHC collision rate of 30 MHz. Allen is the first complete high-throughput GPU trigger proposed for a HEP experiment. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphics processing unit

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Discovery of nine new stellar groups in the orion complex towards a robust unsupervised approach

arXiv

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arXiv 2019年

作者： Chen, Boquan D'Onghia, Elena Alves, João Adamo, Angela Sydney Institute for Astronomy University of Sydney School of Physics A28 CamperdownNSW2006 Australia Department of Astronomy University of Wisconsin 475 North Charter Street MadisonWI53706 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York10010 United States Department of Astrophysics University of Vienna Türkenschanzstrasse 17 Wien1180 Austria Radcliffe Institute for Advanced Study Harvard University 10 Garden Street CambridgeMA02138 United States University of Vienna Faculty of Computer Science Data Science - Uni Vienna Department of Astronomy Stockholm University Oscar Klein Centre AlbaNova University Centre Stockholm106 91 Sweden

We use two unsupervised machine learning algorithms, Shared Nearest Neighbor (SNN) and EnLink, as a single approach to identify stellar groupings in the Orion star-forming complex as an application to the 5-dimensional astrometric data from Gaia DR2. The algorithms present two different ways to limit user bias when evaluating the relative weights among the astrometric parameters, automatically determined by the machine and through a standard procedure by monitoring several outcome measures. Both algorithms complement each other and produce similar stellar groups. Because SNN groups have a much smaller spread in proper motions compared to EnLink, we use EnLink, which requires no input, as a first pass tool for group identification and validation. We then used the SNN algorithm to dissect the Orion star-forming complex. We identify 21 spatially- and kinematically-coherent groups in the Orion complex, nine of which previously unknown. The groups show a wide distribution of distances extending as far as about 150 pc in front of the star-forming Orion clouds, to about 50 pc beyond them where we find, unexpectedly, three groups. Our results expose to view the wealth of sub-structure in the OB association, within and beyond the classical Blaauw Orion OBI sub-groups. A full characterization of the new groups is of the essence as it offers the potential to unveil how star formation proceeds globally in large complexes such as Orion. The data and code that generated the groups is provided in this Letter. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometrical optics

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

arXiv

引用

arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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Semivariogram fitting based on SVM and GPR for DEM interpolation

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IOP Conference Series: Earth and Environmental science 2019年第1期311卷

作者： A Setiyoko A M Arymurthy T Basaruddin R Arief Faculty of Computer Science University of Indonesia Jl. Margonda Raya Pondok Cina Beji Depok 16424 Indonesia Remote Sensing Data Centre-LAPAN Jl. Lapan No.70 Jakarta Timur Jakarta 13710 Indonesia

DEM (Digital Elevation Model) as a digital model of the earth's surface elevation could be generated from remote sensing technology such as stereo imaging for various applications. To generate DEM from stereo imagery, interpolation or approximation process stage is required. Stochastic interpolation e.g. ordinary kriging uses semivariogram fitting to calculate weights of interpolation values based on known points. This research is applying regression types of machine learning for semivariogram fitting to interpolate DEM. Previous research conducted was LS-SVM (Least Square-Support Vector Machine) and SVR (Support Vector Regression) for semivariogram fitting process. Types of SVM and GPR (Gaussian Process Regression) are adopted for semivariogram fitting for ordinary kriging interpolation in this experiment. The result showed that in general SVM types could predict accuracy better than other types of regression, and GPR types produce better DEM accuracy based on the experiment.

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