Publications

2022

Journals

Faber, Jenny*; Kügler, David*; Bahrami, Emad*; Heinz, Lea-Sophie; Timmann, Dagmar; Ernst, Thomas M.; Deike-Hofmann, Katerina; Klockgether, Thoma; van de Warrenburg, Bart; van Gaalen, Judith; Reetz, Kathrin; Romanzetti, Sandro; Oz, Gulin; Joers, James M.; Diedrichsen, Jorn; ESMI MR Study Group; Reuter, Martin (2022): CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. In NeuroImage XX, p. 119703. DOI: 10.1016/j.neuroimage.2022.119703.
*Equal Contribution

Henschel, Leonie*; Kügler, David*; Reuter, Martin (2022): FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI. In NeuroImage 251, p. 118933. DOI: 10.1016/j.neuroimage.2022.118933.
*Equal Contribution

Conferences

Roy, Saikat*; Kügler, David*; Reuter, Martin (2022): Are 2.5D approaches superior to 3D deep networks in whole brain segmentation? In Medical Imaging with Deep Learning. Zürich, Switzerland, 6.7.-8.7.2022. MIDL. Available online at https://openreview.net/forum?id=Ob62JPB_CDF.
*Equal Contribution

Henschel, Leonie; Kügler, David; Andrews, Derek S.; Nordahl, Christine W.; Reuter, Martin (2022): Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions. In Medical Image Computing and Computer Assisted-Intervention. MICCAI. Available online at https://arxiv.org/pdf/2206.14919.

2020

Journals

Kügler, David; Sehring, Jannik; Stefanov, Andrei; Stenin, Igor; Kristin, Julia; Klenzner, Thomas et al. (2020): i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery. In International journal of computer assisted radiology and surgery 15 (7), 1137–1145. Available online at https://link.springer.com/article/10.1007/s11548-020-02157-4.

Krumb, Henry; Hofmann, Sofie; Kügler, David; Ghazy, Ahmed; Dorweiler, Bernhard; Bredemann, Judith et al. (2020): Leveraging spatial uncertainty for online error compensation in EMT. In International journal of computer assisted radiology and surgery 15 (6), pp. 1043–1051. doi: 10.1007/s11548-020-02189-w. Available online at https://link.springer.com/article/10.1007%2Fs11548-020-02189-w

Conferences

Kügler, David*; Uecker, Marc*; Kuijper, Arjan; Mukhopadhyay, Anirban:
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation. In : MICCAI 2020. Available online at http://arxiv.org/pdf/2006.14858.
*Equal contribution

Workshops

Ewert, Christian*, Kügler, David*; Yendiki, Anastasia; Reuter, Martin: Learning Anatomical Segmentations for Tractography from Diffusion MRI. In cdmri’20 Workshop. Available online at http://arxiv.org/pdf/2009.04392.
*Equal contribution

2019

Journals

Kügler, David; Krumb, Henry; Bredemann, Judith; Stenin, Igor; Kristin, Julia; Klenzner, Thomas et al. (2019): High-Precision Evaluation of Electromagnetic Tracking. In International journal of computer assisted radiology and surgery. doi: 10.1007/s11548-019-01959-5

Others

Mukhopadhyay, Anirban; Kügler, David; Bucher, Andreas; Fellner, Dieter; Vogl, Thomas (2019): Putting Trust first in the Translation of AI for Healthcare.In ERCIM News (116), pp. 20–22. Available online at https://ercim-news.ercim.eu/en116/special/putting-trust-first-in-the-translation-of-ai-for-healthcare.

Kügler, David; Bucher, Andreas; Kleemann, Johannes; Distergoft, Alexander; Jabhe, Ali; Uecker, Marc et al. (2018): Physical World Attacks: An evaluation of robustness for Deep-Learning in Dermoscopy. Available online at https://openreview.net/pdf?id=Byl6W7WeeN.

2018

Workshops & Other

Kügler, David; Jastrzebski, Martin; Mukhopadhyay, Anirban (2018):
Instrument Pose Estimation using Registration for Otobasis Surgery.
In 8th International Workshop on Biomedical Image Registration, WBIR2018. doi: 10.1007/978-3-319-92258-4_10 .

Kügler, David; Distergoft, Alexander; Kuijper, Arjan; Mukhopadhyay, Anirban (2018): Exploring Adversarial Examples: Patterns of One-Pixel Attacks. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications, vol. 11038. Cham: Springer, pp. 70–78. Available online at http://arxiv.org/pdf/1806.09410.

Kügler, David; Mukhopadhyay, Anirban (2018): How Bad is Good enough. Noisy annotations for instrument pose estimation. Available online at http://arxiv.org/pdf/1806.07836.