K
Kai Roman Laukamp
Researcher at University Hospitals of Cleveland
Publications - 50
Citations - 794
Kai Roman Laukamp is an academic researcher from University Hospitals of Cleveland. The author has contributed to research in topics: Medicine & Image quality. The author has an hindex of 11, co-authored 41 publications receiving 377 citations. Previous affiliations of Kai Roman Laukamp include University of Cologne & Case Western Reserve University.
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Journal ArticleDOI
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.
Kai Roman Laukamp,Frank Thiele,Georgy Shakirin,David Zopfs,Andrea Faymonville,Marco Timmer,David Maintz,Michael Perkuhn,Jan Borggrefe +8 more
TL;DR: The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity.
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Immune Checkpoint Inhibitor Therapy-related Pneumonitis: Patterns and Management
TL;DR: The mechanism of ICIs and ICI therapy complications are reviewed, with subsequent management techniques and illustrations of the various radiologic patterns of ICI-therapy related pneumonitis.
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CT metal artifacts in patients with total hip replacements: for artifact reduction monoenergetic reconstructions and post-processing algorithms are both efficient but not similar
Kai Roman Laukamp,Simon Lennartz,V Neuhaus,Nils Grosse Hokamp,Robert Rau,Markus Le Blanc,Nuran Abdullayev,Anastasios Mpotsaris,David Maintz,Jan Borggrefe +9 more
TL;DR: Spectral-detector computed tomography improves assessment of total hip replacements and surrounding tissue and Evaluation of bone, muscle and pelvic organs can be improved by SDCT.
Journal ArticleDOI
Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.
Kai Roman Laukamp,Georgy Shakirin,Bettina Baeßler,Frank Thiele,David Zopfs,Nils Große Hokamp,Marco Timmer,Christoph Kabbasch,Michael Perkuhn,Jan Borggrefe +9 more
TL;DR: It is indicated, that radiomics-based feature analysis applied on routine MR is viable for meningioma grading, and a multivariate-logistic-regression-model yielded strong classification performances.
Journal ArticleDOI
Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.
Kai Roman Laukamp,Kai Roman Laukamp,Kai Roman Laukamp,Lenhard Pennig,Frank Thiele,Frank Thiele,Robert Peter Reimer,L Görtz,Georgy Shakirin,Georgy Shakirin,David Zopfs,Marco Timmer,Michael Perkuhn,Michael Perkuhn,Jan Borggrefe +14 more
TL;DR: Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability, and 55 meningiomas in the validation group were detected by the deep learning model.