Dr. Annika Hänsch

  • Research Scientist
  • Deep Learning
  • Mathematician



Hänsch A, Jenne JW, Upadhyay N, Schmeel C, Purrer V, Wüllner U, Klein J (2022) Deep learning-assisted fully automatic fiber tracking for tremor treatment. Proc. of SPIE Medical Imaging, Image-Guided Procedures, Robotic Interventions, and Modeling. 120342A
Hänsch A, Thielke F, Meine H, Rennebaum S, Froelich MF, Becker LS, Hinrichs JB, Schenk A (2022) Robust Liver Segmentation with Deep Learning Across DCE-MRI Contrast Phases. In: Maier-Hein K, Deserno TM, Handels H, Maier A, Palm C, Tolxdorff T (eds) Bildverarbeitung für die Medizin 2022. Springer Fachmedien Wiesbaden, Wiesbaden, pp 13–18
Konradi J, Zajber M, Stegner S, Czysch C, Corsten S, Betz U, Disch C, Hänsch A, Meine H (2022) Nutzung von künstlicher Intelligenz (KI) in der endoskopischen Schluckdiagnostik. Erste Ergebnisse zur Genauigkeit der KI-basierten Aspirations-Detektionsleistung. Sprachtherapie aktuell: Forschung – Wissen – Transfer. XXXIV. Workshop Klinische Linguistik. pp e2022–2002
Peisen F, Hänsch A, Hering A, Brendlin AS, Afat S, Nikolaou K, Gatidis S, Eigentler T, Amaral T, Moltz JH, Othman AE (2022) Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy. Cancers 14(12):2992
Thielke F, Kock F, Hänsch A, Georgii J, Abolmaali N, Endo I, Meine H, Schenk A (2022) Improving deep learning based liver vessel segmentation using automated connectivity analysis. Proc. SPIE Medical Imaging 2022: Image Processing. Proc.SPIE 12032, pp 886–892


Hänsch A (2021) Implications of Dataset Heterogeneity on Deep Learning Performance in Medical Image Segmentation. Ph.D. thesis


Hänsch A, Moltz JH, Geisler B, Engel C, Klein J, Genghi A, Schreier J, Morgas T, Haas B (2020) Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours. J Med Imaging 7(6):064001
Klein J, Wenzel M, Romberg D, Köhn A, Kohlmann P, Link F, Hänsch A, Dicken V, Stein R, Haase J, Schreiber A, Hahn H, Meine H (2020) QuantMed: Component-based DL platform for translational research. Proceedings of SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications. 113180U:pp 1–8
Moltz JH, Hänsch A, Lassen-Schmidt B, Haas B, Genghi A, Schreier J, Morgas T, Klein J (2020) Learning a Loss Function for Segmentation: A Feasibility Study. IEEE International Symposium on Biomedical Imaging. pp 957–960


Dicken V, Hänsch A, Moltz J, Haas B, Coradi T, Morgas T, Klein J (2019) Quantitative and qualitative methods for efficient evaluation of multiple 3D organ segmentations. Proceedings of SPIE Medical Imaging: Image Processing. 1094914:pp 1–8
Hänsch A, Cheng B, Frey B, Mayer C, Petersen M, Lettow I, Yazdan Shenas F, Thomalla G, Klein J, Hahn HK (2019) Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. LNCS 11796, pp 86–94
Hänsch A, Dicken V, Klein J, Morgas T, Haas B, Hahn HK (2019) Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning. Proceedings of SPIE Medical Imaging: Computer-Aided Diagnosis. 109500T:pp 1–8


Haensch A, Dicken V, Gass T, Morgas T, Klein J, Meine H, Hahn HK (2018) Deep learning based segmentation of organs of the female pelvis in CBCT scans for adaptive radiotherapy using CT and CBCT data. Proceedings of the 32nd International Congress and Exhibition of Computer Assisted Radiology and Surgery (CARS). pp 179–180
Hänsch A, Gass T, Morgas T, Haas B, Meine H, Klein J, Hahn HK (2018) Parotid gland segmentation with deep learning using clinical vs. curated training data. Radiotherapy and Oncology: Proceedings of ESTRO 37. pp S281–S282
Hänsch A, Schwier M, Gass T, Morgas T, Haas B, Dicken V, Meine H, Klein J, Hahn HK (2018) Evaluation of deep learning methods for parotid gland segmentation from CT images. J Med Imag 6(1):011005
Hänsch A, Schwier M, Gass T, Morgasz T, Haas B, Klein J, Hahn HK (2018) Comparison of different deep learning approaches for parotid gland segmentation from CT images. Proceedings of SPIE Medical Imaging: Computer-Aided Diagnosis. 1057519:pp 1–6
Weber L, Hänsch A, Wolfram U, Pacureanu A, Cloetens P, Peyrin F, Rit S, Langer M (2018) Registration of phase contrast images in propagation-based X-ray phase tomography. J Microsc 269(1):36–47


Haensch A, Harz M, Schenk A, Endo I, Jacobs C, Hahn HK, Meine H (2017) Examining the robustness of liver segmentation using deep learning and unsupervised pre-training of a feature extractor. International Journal of Computer Assisted Radiology and Surgery. Springer International Publishing, pp 23–24