Manuel Debic
6 Papers
Manuel Debic is an academic researcher. The author has contributed to research in topics: Medicine & Neurology. The author has an hindex of 1, co-authored 4 publications.
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Papers
Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules
Alan A. Peters,Oliver Weinheimer,Oyunbileg von Stackelberg,Jonas Kroschke,Lars Piskorski,Manuel Debic,Kai Schlamp,Linn Welzel,Moritz Pohl,Andreas Christe,Lukas Ebner,Hans-Ulrich Kauczor,Claus Peter Heussel,Mark O. Wielpütz +13 more
TL;DR: In this article , the authors evaluated the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation.
Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules.
Alan A. Peters,Justin B Solomon,Oyunbileg von Stackelberg,Ehsan Samei,Njood Alsaihati,Waldo Valenzuela,Manuel Debic,Christian Heidt,A. T. Huber,Andreas Christe,Johannes T Heverhagen,H. Kauczor,Claus Peter Heussel,Lukas Ebner,Mark O. Wielpütz +14 more
TL;DR: CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management, especially in high-risk cohorts.
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e-Lung CT biomarkers are associated with outcomes in fibrotic interstitial lung diseases
Katharina Abbasi Dezfouli,A. Devaraj,F. Ottink,C. Rennison-Jones,A. Sifostratoudaki,C. Fernandez,Oyunbileg von Stackelberg,Hans-Ulrich Kauczor,Manuel Debic,K. Schlamp,K. Buschulte,C. P. Heussel,J. Briggs,S. Gerry,G. Harston,W. Bou-Zeid,O. Joly,Peter M. George,Michael Kreuter +18 more
TL;DR: This study evaluates the e-Lung weighted reticulovascular score (WRVS) as a prognostic factor in non-IPF fibrotic interstitial lung diseases, finding associations with mortality and forced vital capacity decline in two cohorts of patients.
AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine
Viktoria Palm,Tobias Norajitra,Oyunbileg von Stackelberg,Claus Peter Heussel,Stephan Skornitzke,Oliver Weinheimer,T. Kopytova,André Klein,Sílvia Alexandra Dias Almeida,Michael Baumgartner,Dimitrios Bounias,Jonas Scherer,Klaus Kades,Hanno Gao,Paul Ferdinand Jäger,Marco Nolden,E. Tong,Kira Eckl,Johanna Nattenmüller,Tobias Nonnenmacher,Omar Naas,Julia Reuter,Arved Bischoff,Jonas Kroschke,Fabian Rengier,Kai Schlamp,Manuel Debic,Hans-Ulrich Kauczor,Klaus H. Maier-Hein,Mark O. Wielpütz +29 more
TL;DR: In this article , an interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT).
Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma
Markus Wennmann,Peter F. Neher,Nikolas Stanczyk,Kim-Celine Kahl,Jessica Kächele,V. Weru,Thomas Hielscher,Martin Grözinger,Jiri Chmelik,Ke Zhang,Fabian Bauer,Tobias Nonnenmacher,Manuel Debic,Sandra Sauer,Lukas T. Rotkopf,Anna Jauch,Kai Schlamp,Elias K. Mai,Niels Weinhold,Saif Afat,Marius Horger,Hartmut Goldschmidt,Heinz Peter Schlemmer,Tim F Weber,Stefan Delorme,Felix T. Kurz,Klaus H. Maier-Hein +26 more
TL;DR: A nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations, and can help to overcome interrater variability or nonrepresentative measurements.