Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Jonathan Frederik Carlsen"'
Autor:
Peter Jagd Sørensen, Claes Nøhr Ladefoged, Vibeke Andrée Larsen, Flemming Littrup Andersen, Michael Bachmann Nielsen, Hans Skovgaard Poulsen, Jonathan Frederik Carlsen, Adam Espe Hansen
Publikováno v:
Tomography, Vol 10, Iss 9, Pp 1397-1410 (2024)
The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examin
Externí odkaz:
https://doaj.org/article/82109056b1184a5a93cf9a7cbd10d27d
Autor:
Alexander Malcolm Rykkje, Jonathan Frederik Carlsen, Vibeke Andrée Larsen, Jane Skjøth-Rasmussen, Ib Jarle Christensen, Michael Bachmann Nielsen, Hans Skovgaard Poulsen, Thomas Haargaard Urup, Adam Espe Hansen
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total
Externí odkaz:
https://doaj.org/article/97a9dfc337114ad7912d092d25ccd4ea
Autor:
Jacob Johansen, Cecilie Mørck Offersen, Jonathan Frederik Carlsen, Silvia Ingala, Adam Espe Hansen, Michael Bachmann Nielsen, Sune Darkner, Akshay Pai
Publikováno v:
Diagnostics, Vol 14, Iss 1, p 69 (2023)
DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issue
Externí odkaz:
https://doaj.org/article/720ab026cdc34323b12a9f3c20d08dd5
Autor:
Jane Maestri Brittain, Michael Stormly Hansen, Jonathan Frederik Carlsen, Andreas Hjelm Brandt, Lene Terslev, Mads Radmer Jensen, Ulrich Lindberg, Henrik Bo Wiberg Larsson, Steffen Heegaard, Uffe Møller Døhn, Oliver Niels Klefter, Anne Katrine Wiencke, Yousif Subhi, Steffen Hamann, Bryan Haddock
Publikováno v:
Diagnostics, Vol 14, Iss 1, p 81 (2023)
In order to support or refute the clinical suspicion of cranial giant cell arteritis (GCA), a supplemental imaging modality is often required. High-resolution black blood Magnetic Resonance Imaging (BB MRI) techniques with contrast enhancement can vi
Externí odkaz:
https://doaj.org/article/f4b8eb9142b241e18662a9b5376fb323
Autor:
Cecilie Mørck Offersen, Jens Sørensen, Kaining Sheng, Jonathan Frederik Carlsen, Annika Reynberg Langkilde, Akshay Pai, Thomas Clement Truelsen, Michael Bachmann Nielsen
Publikováno v:
Diagnostics, Vol 13, Iss 12, p 2111 (2023)
We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)—mismatch assessment and to
Externí odkaz:
https://doaj.org/article/98021e52f48549fcae3ba7a3498d085a
Autor:
Dana Li, Lea Marie Pehrson, Rasmus Bonnevie, Marco Fraccaro, Jakob Thrane, Lea Tøttrup, Carsten Ammitzbøl Lauridsen, Sedrah Butt Balaganeshan, Jelena Jankovic, Tobias Thostrup Andersen, Alyas Mayar, Kristoffer Lindskov Hansen, Jonathan Frederik Carlsen, Sune Darkner, Michael Bachmann Nielsen
Publikováno v:
Diagnostics, Vol 13, Iss 6, p 1070 (2023)
A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would com
Externí odkaz:
https://doaj.org/article/c5bc1d8aa10645a5ae6a02206b6ca006
Autor:
Alexander Malcolm Rykkje, Vibeke Andrée Larsen, Jane Skjøth-Rasmussen, Michael Bachmann Nielsen, Jonathan Frederik Carlsen, Adam Espe Hansen
Publikováno v:
Diagnostics, Vol 13, Iss 4, p 795 (2023)
An early postoperative MRI is recommended following Glioblastoma surgery. This retrospective, observational study aimed to investigate the timing of an early postoperative MRI among 311 patients. The patterns of the contrast enhancement (thin linear,
Externí odkaz:
https://doaj.org/article/00a2a82c245749508b533d81d26f5be2
Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI
Autor:
Peter Jagd Sørensen, Jonathan Frederik Carlsen, Vibeke Andrée Larsen, Flemming Littrup Andersen, Claes Nøhr Ladefoged, Michael Bachmann Nielsen, Hans Skovgaard Poulsen, Adam Espe Hansen
Publikováno v:
Diagnostics, Vol 13, Iss 3, p 363 (2023)
In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation
Externí odkaz:
https://doaj.org/article/928982add86448a38e3ec258721352eb
Autor:
Dana Li, Lea Marie Pehrson, Lea Tøttrup, Marco Fraccaro, Rasmus Bonnevie, Jakob Thrane, Peter Jagd Sørensen, Alexander Rykkje, Tobias Thostrup Andersen, Henrik Steglich-Arnholm, Dorte Marianne Rohde Stærk, Lotte Borgwardt, Kristoffer Lindskov Hansen, Sune Darkner, Jonathan Frederik Carlsen, Michael Bachmann Nielsen
Publikováno v:
Diagnostics, Vol 12, Iss 12, p 3112 (2022)
Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The p
Externí odkaz:
https://doaj.org/article/7486627be9e549c5a7d73f768d8bf787
Autor:
Kaining Sheng, Cecilie Mørck Offersen, Jon Middleton, Jonathan Frederik Carlsen, Thomas Clement Truelsen, Akshay Pai, Jacob Johansen, Michael Bachmann Nielsen
Publikováno v:
Diagnostics, Vol 12, Iss 8, p 1878 (2022)
We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubM
Externí odkaz:
https://doaj.org/article/1fa2fecb12a144138800906e83db14f9