K
Klas E. G. Magnusson
Researcher at Royal Institute of Technology
Publications - 20
Citations - 3647
Klas E. G. Magnusson is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Stem cell & Viterbi algorithm. The author has an hindex of 10, co-authored 17 publications receiving 3032 citations. Previous affiliations of Klas E. G. Magnusson include Stanford University.
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Journal ArticleDOI
Substrate Elasticity Regulates Skeletal Muscle Stem Cell Self-Renewal in Culture
Penney M. Gilbert,Karen Havenstrite,Klas E. G. Magnusson,Klas E. G. Magnusson,Alessandra Sacco,Nora Leonardi,Nora Leonardi,Peggy E. Kraft,N. K. Nguyen,Sebastian Thrun,Matthias P. Lutolf,Helen M. Blau +11 more
TL;DR: Using a bioengineered substrate to recapitulate key biophysical and biochemical niche features in conjunction with a highly automated single-cell tracking algorithm, it is shown that substrate elasticity is a potent regulator of MuSC fate in culture.
Journal ArticleDOI
Objective comparison of particle tracking methods
Nicolas Chenouard,Ihor Smal,Fabrice de Chaumont,Martin Maška,Martin Maška,Ivo F. Sbalzarini,Yuanhao Gong,Janick Cardinale,Craig Carthel,Stefano Coraluppi,Mark R. Winter,Andrew R. Cohen,William J. Godinez,Karl Rohr,Yannis Kalaidzidis,Liang Liang,James S. Duncan,Hongying Shen,Yingke Xu,Klas E. G. Magnusson,Joakim Jalden,Helen M. Blau,Perrine Paul-Gilloteaux,Philippe Roudot,Charles Kervrann,François Waharte,Jean-Yves Tinevez,Spencer L. Shorte,Joost Willemse,Katherine Celler,Gilles P. van Wezel,Han-Wei Dan,Yuh-Show Tsai,Carlos Ortiz de Solórzano,Jean-Christophe Olivo-Marin,Erik Meijering +35 more
TL;DR: Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
Journal ArticleDOI
An objective comparison of cell-tracking algorithms
Vladimír Ulman,Martin Maška,Klas E. G. Magnusson,Olaf Ronneberger,Carsten Haubold,Nathalie Harder,Pavel Matula,Petr Matula,David Svoboda,Miroslav Radojevic,Ihor Smal,Karl Rohr,Joakim Jalden,Helen M. Blau,Oleh Dzyubachyk,Boudewijn P. F. Lelieveldt,Boudewijn P. F. Lelieveldt,Pengdong Xiao,Yuexiang Li,Siu-Yeung Cho,Alexandre Dufour,Jean-Christophe Olivo-Marin,Constantino Carlos Reyes-Aldasoro,Jose Alonso Solis-Lemus,Robert Bensch,Thomas Brox,Johannes Stegmaier,Ralf Mikut,Steffen Wolf,Fred A. Hamprecht,Tiago Esteves,Pedro Quelhas,Omer Burak Demirel,Lars Malmström,Florian Jug,Pavel Tomancak,Erik Meijering,Arrate Muñoz-Barrutia,Michal Kozubek,Carlos Ortiz-de-Solorzano +39 more
TL;DR: It is found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the Cell Tracking Challenge.
Journal ArticleDOI
A benchmark for comparison of cell tracking algorithms
Martin Maška,Vladimír Ulman,David Svoboda,Pavel Matula,Petr Matula,Cristina Ederra,Ainhoa Urbiola,Tomás España,Subramanian Venkatesan,Deepak M.W. Balak,Pavel Karas,Tereza Bolcková,Markéta Štreitová,Craig Carthel,Stefano Coraluppi,Nathalie Harder,Karl Rohr,Klas E. G. Magnusson,Joakim Jalden,Helen M. Blau,Oleh Dzyubachyk,Pavel Křížek,Guy M. Hagen,David Pastor-Escuredo,Daniel Jimenez-Carretero,Maria J. Ledesma-Carbayo,Arrate Muñoz-Barrutia,Erik Meijering,Michal Kozubek,Carlos Ortiz-de-Solorzano +29 more
TL;DR: Six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets in the Cell Tracking Challenge.
Journal ArticleDOI
Global Linking of Cell Tracks Using the Viterbi Algorithm
TL;DR: A global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks, which can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells.