Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Måns Larsson"'
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-17 (2024)
Abstract Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversar
Externí odkaz:
https://doaj.org/article/5be8ad311a284e288cfa346f65747650
Autor:
David Molnar, Elias Björnson, Måns Larsson, Martin Adiels, Anders Gummesson, Fredrik Bäckhed, Ola Hjelmgren, Göran Bergström
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract The volume of epicardial adipose tissue (EATV) is increased in type-2 diabetes (T2D), while its attenuation (EATA) appears to be decreased. Similar patterns have been suggested in pre-diabetes, but data is scarce. In both pre-diabetes and T2
Externí odkaz:
https://doaj.org/article/8d5489de06ee4073b1902f8e79a27cff
Autor:
David Molnar, Olof Enqvist, Johannes Ulén, Måns Larsson, John Brandberg, Åse A. Johnsson, Elias Björnson, Göran Bergström, Ola Hjelmgren
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardi
Externí odkaz:
https://doaj.org/article/38fe4a3e3bd642fca495bdaff7ad75c5
Autor:
Sarah Lindgren Belal, Måns Larsson, Jorun Holm, Karen Middelbo Buch-Olsen, Jens Sörensen, Anders Bjartell, Lars Edenbrandt, Elin Trägårdh
Publikováno v:
European Journal of Nuclear Medicine and Molecular Imaging. 50:1510-1520
Purpose Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumo
Autor:
Reza Piri, Yaran Hamakan, Ask Vang, Lars Edenbrandt, Måns Larsson, Olof Enqvist, Oke Gerke, Poul Flemming Høilund‐Carlsen
Publikováno v:
Clinical Physiology and Functional Imaging. 43:71-77
Autor:
Reza Piri, Amalie H. Nøddeskou‐Fink, Oke Gerke, Måns Larsson, Lars Edenbrandt, Olof Enqvist, Poul‐Flemming Høilund‐Carlsen, Mette J. Stochkendahl
Publikováno v:
Clinical Physiology and Functional Imaging. 42:225-232
Current imaging modalities are often incapable of identifying nociceptive sources of low back pain (LBP). We aimed to characterize these by means of positron emission tomography/computed tomography (PET/CT) of the lumbar spine region applying tracers
BackgroundAutomated organ segmentation in computed tomography (CT) is a vital component in many artificial intelligence-based tools in medical imaging. This study presents a new organ segmentation tool called Organ Finder 2.0. In contrast to most exi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::603bff9d1ef8e278eb7fe5c4d96e475c
https://doi.org/10.1101/2022.11.15.22282357
https://doi.org/10.1101/2022.11.15.22282357
Autor:
Måns Larsson, Poul Flemming Høilund-Carlsen, Lars Edenbrandt, Sofie Skovrup, Babak Saboury, Abass Alavi, Reza Piri, Oke Gerke, Olof Enqvist, Kasper Iversen
Publikováno v:
Piri, R, Edenbrandt, L, Larsson, M, Enqvist, O, Skovrup, S, Iversen, K K, Saboury, B, Alavi, A, Gerke, O & Høilund-Carlsen, P F 2022, ' “Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18 F-sodium fluoride PET/CT scans : Head-to-head comparison ', Journal of Nuclear Cardiology, vol. 29, no. 5, pp. 2531-2539 . https://doi.org/10.1007/s12350-021-02758-9
Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomogr
Autor:
Poul Flemming Høilund-Carlsen, Reza Piri, Lars Edenbrandt, Olof Enqvist, Måns Larsson, Amalie Horstmann Nøddeskou-Fink, Oke Gerke
Publikováno v:
Journal of Nuclear Cardiology. 29:2001-2010
We aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. For segmentation of the wall in three sections: the arch, thoracic, and abdomina
Autor:
Elin Trägårdh, Måns Larsson, Poul Flemming Høilund-Carlsen, Mike Allan Mortensen, Lars Edenbrandt, Pablo Borrelli, Olof Enqvist, Johannes Ulén, Henrik Kjölhede, Mads Hvid Poulsen
Publikováno v:
Borrelli, P, Larsson, M, Ulén, J, Enqvist, O, Trägårdh, E, Poulsen, M H, Mortensen, M A, Kjölhede, H, Høilund-Carlsen, P F & Edenbrandt, L 2021, ' Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival ', Clinical Physiology and Functional Imaging, vol. 41, no. 1, pp. 62-67 . https://doi.org/10.1111/cpf.12666
Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods ca