Dr. Annika Gerken

Publications

2024

Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga A-I, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer H-P, Schlett CL, Maier-Hein K, Bamberg F (2024) Radiomics workflow definition & challenges – German priority program 2177 consensus statement on clinically applied radiomics. Insights Imaging 15(1)

Hering A, Westphal M, Gerken A, Almansour H, Maurer M, Geisler B, Kohlbrandt T, Eigentler T, Amaral T, Lessmann N, Gatidis S, Hahn H, Nikolaou K, Othman A, Moltz J, Peisen F (2024) Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow. International Journal of Computer Assisted Radiology and Surgery 19:1689–1697

Peisen F, Gerken A, Dahm I, Nikolaou K, Eigentler T, Amaral T, Moltz JH, Othman AE, Gatidis S (2024) Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy. PLoS ONE 19(1):e0296253

Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE (2024) Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors? Cancers 16(15):2669

Walluscheck S, Gerken A, Galinovic I, Villringer K, Fiebach JB, Klein J, Heldmann S (2024) Generative adversarial network–based reconstruction of healthy anatomy for anomaly detection in brain CT scans. JMI 11(4)

2023

Ambroladze A, Hahn HK, Amer H, Ingrisch M, Gerken A, Wenzel M, Püsken M, Mittermeier A, Engel C, Schmutzler R, Fallenberg EM (2023) CNN-based Whole Breast Segmentation in Longitudinal High-risk MRI Study. Bildverarbeitung fuer die Medizin. pp 159–164

Gerken A, Thielke F, Meine H, Singer S, Froelich MF, Becker LS, Hinrichs J, Schenk A (2023) Importance of the late hepatobiliary phase of DCE-MRI for automatic liver lesion detection and segmentation. European Congress of Radiology

Gerken A, Walluscheck S, Kohlmann P, Galinovic I, Villringer K, Fiebach JB, Klein J, Heldmann S (2023) Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies. Front. Neuroimaging 2:1228255

Klein J, Gerken A, Agethen N, Rothlübbers S, Upadhyay N, Purrer V, Schmeel C, Borger V, Kovalevsky M, Rachmilevitch I, Shapira Y, Wüllner U, Jenne J (2023) Automatic planning of MR-guided transcranial focused ultrasound treatment for essential tremor. Front. Neuroimaging, Sec. Brain Imaging Methods 2

Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE (2023) Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study. Diagnostics 13(20):3210

Thielke F, Kock F, Hänsch A, Abolmaali N, Schenk A, Meine H (2023) Combining arterial and venous CT scans in a multi-encoder network for improved hepatic vessel segmentation. Proc. SPIE Medical Imaging 2023: Image Processing. Proc.SPIE 12464, 124640B

2022

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

Hänsch A, Chlebus G, Meine H, Thielke F, Kock F, Paulus T, Abolmaali N, Schenk A (2022) Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks. Sci Rep 12:12262

Konradi J, Zajber M, Betz U, Drees P, Gerken A, Meine H (2022) AI-based detection of aspiration for video-endoscopy with visual aids in meaningful frames to interpret the model outcome. Sensors 22(23):9468

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

2021

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

2020

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

2019

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

2018

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

2017

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

Research Interests