Suchergebnisse

Kloenne, M.; Niehaus, S.; Lampe, L.; Merola, A.; Reinelt, J.; Roeder, I.; Scherf, N.: Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes. Scientific Reports 10 (1), 10712 (2020)

Shah, G.; Thierbach, K.; Schmid, B.; Waschke, J.; Reade, A.; Hlawitschka, M.; Roeder, I.; Scherf, N.; Huisken, J.: Multi-scale imaging and analysis identify pan-embryo cell dynamics of germlayer formation in zebrafish. Nature Communications 10, 5753 (2019)

Burek, P.; Scherf, N.; Herre, H.: Ontology patterns for the representation of quality changes of cells in time. Journal of Biomedical Semantics 10, 16 (2019)

Burek, P.; Scherf, N.; Herre, H.: A pattern-based approach to a cell tracking ontology. Procedia Computer Science 159, S. 784 - 793 (2019)

Morawski, M.; Kirilina, E.; Scherf, N.; Jäger, C.; Reimann, K.; Trampel, R.; Gavriilidis, F.; Geyer, S.; Biedermann, B.; Arendt, T. et al.; Weiskopf, N.: Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology. NeuroImage 182, S. 417 - 428 (2018)

Weber, M.; Scherf, N.; Meyer, A. M.; Panáková, D.; Kohl, P.; Huisken, J.: Cell-accurate optical mapping across the entire developing heart. eLife 6, e28307 (2017)

De Back , W.; Seurig, S.; Wagner, S.; Marre, B.; Roeder, I.; Scherf, N.: Forensic age estimation with Bayesian convolutional neural networks based on panoramic dental X-ray imaging. In: Proceedings of the 2019 International Conference on Medical Imaging with Deep Learning (MIDL). (2019)

Kloenne, M.; Niehaus, S.; Lampe, L.; Merola, A.; Reinelt, J.; Scherf, N.: Convolutional neural network stacking for medical image segmentation in CT scans. In: 2019 Kidney Tumor Segmentation Challenge. MICCAI 2019, Shenzhen, China, 13. Oktober 2019 - 19. Oktober 2019. University of Minnesota Libraries Publishing, Minneapolis, MN (2019)

Thierbach, K.; Bazin, P.-L.; de Back, W.; Gavriilidis, F.; Kirilina, E.; Jäger, C.; Morawski, M.; Geyer, S.; Weiskopf, N.; Scherf, N.: Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics. In: Machine Learning in Medical Imaging 2018, S. 179 - 187. 9th International Workshop on Machine Learning in Medical Imaging, Granada, Spain, 16. September 2018. Springer, Cham (2018)

Brammerloh, M.; Weigelt, I.; Arendt, T.; Gavriilidis, F.; Scherf, N.; Jankuhn, S.; Morawski, M.; Weiskopf, N.; Kirilina, E.: Iron-induced relaxation mechanisms in the human substantia nigra: Towards quantifying iron load in dopaminergic neurons. In Proceedings of the ISMRM 26th Annual Meeting & Exhibition. Joint Annual Meeting ISMRM-ESMRMB 2018, Paris Expo Porte de Versailles, 16. Juni 2018 - 21. Juni 2019. (2018)

Podranski, K.; Zhang, R.; Thierbach, K.; Thieleking, R.; Witte, A. V.; Villringer, A.; Weiskopf, N.; Scherf, N.: Detecting fat artifacts in diffusion MRI with deep learning. 9th IMPRS NeuroCom Summer School, Leipzig, Germany (2019)

Podranski, K.; Scherf, N.; Weiskopf, N.: Improving high-resolution qunatitative MRI maps for in-vivo histology MRI of the human brain. Medical Imaging Summer School 2018 - Medical Imaging Meets Deep Learning, Favignana, Italy (2018)

Thierbach, K.; Bazin, P.-L.; De Back , W.; Gavriilidis, F.; Kirilina, E.; Jäger, C.; Morawski, M.; Geyer, S.; Weiskopf, N.; Scherf, N.: Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics. BioRxiv (2018)

Shah, G.; Thierbach, K.; Schmid, B.; Reade, A.; Roeder, I.; Scherf, N.; Huisken, J.: Pan-embryo cell dynamics of germlayer formation in zebrafish. bioRxiv (2017)

Kloenne, M.; Niehaus, S.; Lampe, L.; Merola, A.; Reinelt, J.; Scherf, N.: A framework for CT image segmentation inspired by the clinical environment. arXiv (2019)