Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer
Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the comp...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 November 2023
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| In: |
Scientific reports
Year: 2023, Jahrgang: 13, Pages: 1-12 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-023-46747-z |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-023-46747-z Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-023-46747-z 
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| Verfasserangaben: | Balint Kovacs, Nils Netzer, Michael Baumgartner, Adrian Schrader, Fabian Isensee, Cedric Weißer, Ivo Wolf, Magdalena Görtz, Paul F. Jaeger, Victoria Schütz, Ralf Floca, Regula Gnirs, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, David Bonekamp & Klaus H. Maier-Hein |
MARC
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| 245 | 1 | 0 | |a Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer |c Balint Kovacs, Nils Netzer, Michael Baumgartner, Adrian Schrader, Fabian Isensee, Cedric Weißer, Ivo Wolf, Magdalena Görtz, Paul F. Jaeger, Victoria Schütz, Ralf Floca, Regula Gnirs, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, David Bonekamp & Klaus H. Maier-Hein |
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| 520 | |a Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose ‘misalignment augmentation’ as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set. | ||
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