Fraunhofer Institute for Digital Medicine MEVIS- Research Scientist
- Image Registration
Sven Kuckertz, M.Sc.
Publications
2023
Hering A, Hansen L, Mok TCW, Chung ACS, Siebert H, Hager S, Lange A, Kuckertz S, Heldmann S, Shao W, Vesal S, Rusu M, Sonn G, Estienne T, Vakalopoulou M, Han L, Huang Y, Yap P-T, Brudfors M, Balbastre Y, Joutard S, Modat M, Lifshitz G, Raviv D, Lv J, Li Q, Jaouen V, Visvikis D, Fourcade C, Rubeaux M, Pan W, Xu Z, Jian B, De Benetti F, Wodzinski M, Gunnarsson N, Sjolund J, Grzech D, Qiu H, Li Z, Thorley A, Duan J, Grosbrohmer C, Hoopes A, Reinertsen I, Xiao Y, Landman B, Huo Y, Murphy K, Lessmann N, van Ginneken B, Dalca AV, Heinrich MP (2023) Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning. IEEE Trans Med Imaging 42(3):697–712
Kuckertz S, Heldmann S, Moltz JH (2023) Efficient Registration of Longitudinal Studies for Follow-Up Lesion Assessment by Exploiting Redundancy and Composition of Deformations. In: Woo J, Hering A, Silva W, Li X, Fu H, et al. (eds) Proceedings of the First MICCAI Workshop on Lesion Evaluation and Assessment with Follow-up (LEAF 2023). LNCS 14394, pp 91–99
2022
Kuckertz S, Klein J, Engel C, Geisler B, Krass S, Heldmann S (2022) Fully automated longitudinal tracking and in-depth analysis of the entire tumor burden: unlocking the complexity. Proc. SPIE Medical Imaging 2022: Computer-Aided Diagnosis. Proc. SPIE 12033, pp 455–459
2021
Kuckertz S, Weiler F, Matusche B, Lukas C, Spies L, Klein J, Heldmann S (2021) A system for fully automated monitoring of lesion evolution over time in multiple sclerosis. Proceedings of SPIE Medical Imaging: Computer-Aided Diagnosis. 115972D
2020
Kuckertz S, Papenberg N, Honegger J, Morgas T, Haas B, Heldmann S (2020) Deep learning based CT-CBCT image registration for adaptive radio therapy. Proceedings of SPIE Medical Imaging: Image Processing. 113130Q:pp 1–6
Kuckertz S, Papenberg N, Honegger J, Morgas T, Haas B, Heldmann S (2020) Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy. In: Špiclin Ž, McClelland J, Kybic J, Goksel O (eds) Biomedical Image Registration. LNCS 12120, pp 44–53
2019
Hering A, Kuckertz S, Heldmann S, Heinrich MP (2019) Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking. Bildverarbeitung für die Medizin 2019. pp 309–314
Hering A, Kuckertz S, Heldmann S, Heinrich MP (2019) Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans. Int J CARS 14(11):1901–1912
2018
Kuckertz S (2018) Schwach überwachtes Lernen nichtlinearer medizinischer Bildregistrierung mit neuronalen Faltungsnetzwerken. Master's thesis
