2023

• S. Kolibová, E. Wölfel, H. Hemmatian, P. Milovanovic, H. Mushumba, B. Wulff, M. Neidhardt, K. Püschel, A. Failla, A. Vlug, A. Schlaefer, B. Ondruschka, M. Amling, L. Hofbauer, M. Rauner, B. Busse, K. Jähn-Rickert (2023). Osteocyte apoptosis and cellular micropetrosis signify skeletal aging in type 1 diabetes. Acta Biomaterialia. [Abstract] [doi] [BibTex] [pmid]
  
  
• M. Neidhardt, R. Mieling, M. Bengs, A. Schlaefer (2023). Optical force estimation for interactions between tool and soft tissues. Scientific Reports. 13. (1), 506 [Abstract] [doi] [www] [BibTex]
  
  

2022

• M. Bengs, F. Behrendt, M.-H. Laves, J. Krüger, R. Opfer, A. Schlaefer (2022). Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction. In Karen Drukker and Khan M. Iftekharuddin and Hongbing Lu and Maciej A. Mazurowski and Chisako Muramatsu and Ravi K. Samala (Eds.) Medical Imaging 2022: Computer-Aided Diagnosis SPIE: 1203314 [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya, B. T. Becker, F. Behrendt, M. Bengs, D. Beyersdorff, D. Eggert, E. Petersen, F. Jansen, M. Petersen, B. Cheng, C. Betz, A. Schlaefer, A. S. Hoffmann (2022). Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus. In Wang, Linwei, Dou, Qi, Fletcher, P. Thomas, Speidel, Stefanie, Li, Shuo (Eds.) Medical Image Computing, Computer Assisted Intervention -- MICCAI 2022 Springer Nature Switzerland: Cham 429-438 [Abstract] [doi] [www] [BibTex]
  
  
• J. Sprenger, M. Neidhardt, S. Latus, S. Grube, M. Fischer, A. Schlaefer (2022). Surface Scanning for Navigation Using High-Speed Optical Coherence Tomography. Current Directions in Biomedical Engineering. 8. (1), 62-65 [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya, D. Eggert, C. Betz, A. Schlaefer (2022). Squeeze, multi-context attention for polyp segmentation. International Journal of Imaging Systems, Technology. Accepted [Abstract] [doi] [www] [BibTex]
  
  
• F. Behrendt, D. Bhattacharya, J. Krüger, R. Opfer, A. Schlaefer (2022). Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs. Current Directions in Biomedical Engineering. 8. (1), 34-37 [Abstract] [doi] [www] [BibTex]
  
  
• S. Grube, M. Neidhardt, S. Latus, A. Schlaefer (2022). Influence of the Field of View on Shear Wave Velocity Estimation. Current Directions in Biomedical Engineering. 8. (1), 42-45 [Abstract] [doi] [www] [BibTex]
  
  
• L. Maack, L. Holstein, A. Schlaefer (2022). GANs for generation of synthetic ultrasound images from small datasets. Current Directions in Biomedical Engineering. 8. (1), 17-20 [Abstract] [doi] [www] [BibTex]
  
  
• D. Eggert, M. Bengs, S. Westermann, N. Gessert, A. O. H. Gerstner, N. A. Mueller, J. Bewarder, A. Schlaefer, C. Betz, , W. Laffers (2022). In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods. Journal of Biophotonics. 15. (3), e202100167 [Abstract] [doi] [www] [BibTex]
  
  
• R. Mieling, C. Stapper, S. Gerlach, M. Neidhardt, S. Latus, M. Gromniak, P. Breitfeld, A. Schlaefer (2022). Proximity-Based Haptic Feedback for Collaborative Robotic Needle Insertion. In Seifi, Hasti and Kappers, Astrid M. L. and Schneider, Oliver and Drewing, Knut and Pacchierotti, Claudio and Abbasimoshaei, Alireza and Huisman, Gijs and Kern, Thorsten A. (Eds.) Haptics: Science, Technology, Applications Springer International Publishing: Cham 301-309 [Abstract] [BibTex]
  
  
• S. Gerlach, T. Hofmann, C. Fuerweger, A. Schlaefer (2022). TH-B-206-02: Fast Adaptive Replanning by Constrained Reoptimization for Intra-Fractional Non-Periodic Motion During SBRT of the Prostate. Medical Physics E570-E570 [Abstract] [www] [BibTex]
  
  
• J. Sprenger, M. Bengs, S. Gerlach, M. Neidhardt, A. Schlaefer (2022). Systematic analysis of volumetric ultrasound parameters for markerless 4D motion tracking. International Journal of Computer Assisted Radiology, Surgery. [Abstract] [doi] [www] [BibTex]
  
  
• T. Sonntag, M. Bauer, J. Sprenger, S. Gerlach, P. Breitfeld, A. Schlaefer (2022). Deep learning based segmentation of cervical blood vessels in ultrasound images. The European Anaesthesiology Congress, Euroanaesthesia 2022 41-41 [Abstract] [www] [BibTex]
  
  
• F. Behrendt, M. Bengs, F. Rogge, J. Krüger, R. Opfer, A. Schlaefer (2022). Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) 1-4 [Abstract] [doi] [BibTex]
  
  
• S. Gerlach, T. Hofmann, C. Fürweger, A. Schlaefer (2022). AI-based optimization for US-guided radiation therapy of the prostate. International Journal of Computer Assisted Radiology, Surgery. [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt, M. Bengs, S. Latus, S. Gerlach, C. J. Cyron, J. Sprenger, A. Schlaefer (2022). Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning. IEEE Transactions on Biomedical Engineering. 1-1 [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya, F. Behrendt, A. Felicio-Briegel, V. Volgger, D. Eggert, C. Betz, A. Schlaefer (2022). Learning Robust Representation for Laryngeal Cancer Classification in Vocal Folds from Narrow Band Images. Medical Imaging with Deep Learning. Accepted [www] [BibTex]
  
  
• F. Behrendt, M. Bengs, D. Bhattacharya, J. Krüger, R. Opfer, A. Schlaefer (2022). Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly Detection. Medical Imaging with Deep Learning. Accepted [www] [BibTex]
  
  
• M. H. Laves, M. Tölle, A. Schlaefer, S. Engelhardt (2022). Posterior temperature optimized Bayesian models for inverse problems in medical imaging. Medical Image Analysis. 78. 102382 [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt, S. Gerlach, R. Mieling, M.-H. Laves, T. Weiß, M. Gromniak, A. Fitzek, D. Möbius, I. Kniep, A. Ron, J. Schädler, A. Heinemann K., Püschel, B. Ondruschka, A. Schlaefer (2022). Robotic Tissue Sampling for Safe Post-Mortem Biopsy in Infectious Corpses. IEEE Transactions on Medical Robotics and Bionics. 4. (1), 94-105 [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, A. Schlaefer (2022). Robotic Systems in Radiotherapy and Radiosurgery. Current Robotics Reports. [Abstract] [doi] [www] [BibTex]
  
  

2021

• K. P. Abdolazizi, K. Linka, J. Sprenger, M. Neidhardt, A. Schlaefer, C. J. Cyron (2021). Identification of the concentration‐dependent viscoelastic constitutive parameters of gelatin by combining computational mechanics, machine learning. Proceedings in applied mathematics, mechanics. 21. (1), e202100250 [Abstract] [www] [BibTex]
  
  
• M. Bengs, F. Behrendt, J. Krüger, R. Opfer, A. Schlaefer (2021). Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI. International Journal of Computer Assisted Radiology and Surgery. 16. (9), 1413-1423 [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs, M. Bockmayr, U. Schüller, A. Schlaefer (2021). Medulloblastoma tumor classification using deep transfer learning with multi-scale EfficientNets. In John E. Tomaszewski and Aaron D. Ward (Eds.) Medical Imaging 2021: Digital Pathology SPIE: 70-75 [Abstract] [doi] [www] [BibTex]
  
  
• D. B. Ellebrecht, N. Hessler, A. Schlaefer, N. Gessert (2021). Confocal Laser Microscopy for in vivo Intraoperative Application: Diagnostic Accuracy of Investigator and Machine Learning Strategies. [Abstract] [doi] [www] [BibTex]
  
  
• J. Krüger, A. C. Ostwaldt, L. Spies, B. Geisler, A. Schlaefer, H. H. Kitzler, S. Schippling, R. Opfer (2021). Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks. [Abstract] [doi] [www] [BibTex]
  
  
• M. Schlüter (2021). Analysis of ultrasound and optical coherence tomography for markerless volumetric image guidance in robotic radiosurgery. [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt, S. Gerlach, M.-H. Laves, S. Latus, C. Stapper, M. Gromniak, A. Schlaefer (2021). Collaborative robot assisted smart needle placement. Current Directions in Biomedical Engineering. 7. (2), 472-475 [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, M. Neidhardt, T. Weiß, M.-H. Laves, C. Stapper, M. Gromniak, I. Kniep, D. Möbius, A. Heinemann, B. Ondruschka, A. Schlaefer (2021). Needle insertion planning for obstacle avoidance in robotic biopsy. Current Directions in Biomedical Engineering. 7. (2), 779-782 [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya, C. Betz, D. Eggert, A. Schlaefer (2021). Self-Supervised U-Net for Segmenting Flat and Sessile Polyps. SPIE Medical Imaging Symposium 2022 [Abstract] [www] [BibTex]
  
  
• D. Bhattacharya, C. Betz, D. Eggert, A. Schlaefer (2021). Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet. Nordic Machine Intelligence, MedAI2021. 1 (1), 11-13 Second place in challenge task. [Abstract] [doi] [BibTex]
  
  
• S. Lehmann, A. Rogalla, M. Neidhardt, A. Schlaefer S.Schupp (2021). Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles. Electronic Proceedings in Theoretical Computer Science. 348 128-135. [Abstract] [doi] [www] [BibTex]
  
  
• R. Mieling, J. Sprenger, S. Latus, L. Bargsten, A. Schlaefer (2021). A novel optical needle probe for deep learning-based tissue elasticity characterization:. Current Directions in Biomedical Engineering. 7 (1), 21-25. [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt, J. Ohlsen, N. Hoffmann, A. Schlaefer (2021). Parameter Identification for Ultrasound Shear Wave Elastography Simulation:. Current Directions in Biomedical Engineering. 7 (1), 35-38. [Abstract] [doi] [www] [BibTex]
  
  
• J. Sprenger, J. Petersen, N. Neumann, H. Reichenspurner, D. Russ, C. Detter, A. Schlaefer (2021). Tracking heart surface features to determine myocardial contrast agent enrichment:. Current Directions in Biomedical Engineering. 7 (1), 53-57. [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs, S. Pant, M. Bockmayr, U. Schüller, A. Schlaefer (2021). Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning. Current Directions in Biomedical Engineering. 7 (1), 63-66. [Abstract] [doi] [www] [BibTex]
  
  
• L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer (2021). Attention via Scattering Transforms for Segmentation of Small Intravascular Ultrasound Data Sets. In Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris (Eds.) Proceedings of the Fourth Conference on Medical Imaging with Deep Learning PMLR: 34-47. [Abstract] [www] [BibTex]
  
  
• L. Bargsten, D. Klisch, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer (2021). Deep learning for guidewire detection in intravascular ultrasound images:. Current Directions in Biomedical Engineering. 7 (1), 106-110. [Abstract] [doi] [www] [BibTex]
  
  
• L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer (2021). Deep learning for calcium segmentation in intravascular ultrasound images:. Current Directions in Biomedical Engineering. 7 (1), 96-100. [Abstract] [doi] [www] [BibTex]
  
  
• J. F. Fast, H. R. Dava, A. K. Rüppel, D. Kundrat, M. Krauth, M.-H. Laves, S. Spindeldreier, L. A. Kahrs, M. Ptok (2021). Stereo Laryngoscopic Impact Site Prediction for Droplet-Based Stimulation of the Laryngeal Adductor Reflex. IEEE Access. 9 112177-112192. [Abstract] [doi] [BibTex]
  
  
• F. N. Schmidt, S. Gerlach, M. Issleib, A. Schlaefer, B. Busse (2021). Development of a virtual reality-based training for the elderly with increased fracture risk to prevent falls and improve their balance. Bone Reports. 14 100950. [doi] [www] [BibTex]
  
  
• L. Bargsten, S. Raschka, A. Schlaefer (2021). Capsule networks for segmentation of small intravascular ultrasound image datasets. International Journal of Computer Assisted Radiology and Surgery. 16 (8), 1243-1254. [Abstract] [doi] [www] [BibTex]
  
  
• K. P. Abdolazizi, K. Linka, J. Sprenger, M. Neidhardt, A. Schlaefer, C. J. Cyron (2021). Concentration-Specific Constitutive Modeling of Gelatin Based on Artificial Neural Networks. PAMM. 20 (1), e202000284. [Abstract] [doi] [www] [BibTex]
  
  
• S. Latus, J. Sprenger, M. Neidhardt, J. Schadler, A. Ron, A. Fitzek, M. Schlüter, P. Breitfeld, A. Heinemann, K. Püschel, A. Schlaefer (2021). Rupture detection during needle insertion using complex OCT data and CNNs. IEEE Transactions on Biomedical Engineering. 68 (10), 3059-3067. [Abstract] [doi] [BibTex]
  
  
• J. Sprenger, M. Neidhardt, M. Schlüter, S. Latus, T. Gosau, J. Kemmling, S. Feldhaus, U. Schumacher, A. Schlaefer (2021). In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs. In Cristian A. Linte and Jeffrey H. Siewerdsen (Eds.) Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling SPIE: 345 - 350. [Abstract] [doi] [www] [BibTex]
  
  
• J. Sprenger, T. Saathoff, A. Schlaefer (2021). Automated robotic surface scanning with optical coherence tomography. IEEE 18th International Symposium on Biomedical Imaging 1137-1140. [Abstract] [BibTex]
  
  
• L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, J. Sprenger, M. Grass, M. Seiffert, S. Blankenberg, A. Schlaefer (2021). Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks. In Brett C. Byram and Nicole V. Ruiter (Eds.) Medical Imaging 2021: Ultrasonic Imaging and Tomography SPIE: 1-7. [Abstract] [doi] [www] [BibTex]
  
  
• J. Ohlsen, M. Neidhardt, A. Schlaefer, N. Hoffmann (2021). Modelling shear wave propagation in soft tissue surrogates using a finite element- and finite difference method. PAMM. 20 (1), e202000148. [Abstract] [doi] [www] [BibTex]
  
  

2020

• M. Bengs, N. Gessert, A. Schlaefer (2020). 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification. arXiv: [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs, S. Westermann, N. Gessert, D. Eggert, A. O. H. Gerstner, N. A. Mueller, C. Betz, W. Laffers, A. Schlaefer (2020). Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection. In Horst K. Hahn, Maciej A. Mazurowski (Eds.) Medical Imaging 2020: Computer-Aided Diagnosis SPIE: 113141L [Abstract] [doi] [www] [BibTex]
  
  
• R. Mieling, S. Latus, N. Gessert, M. Lutz, A. Schlaefer (2020). Deep learning-based rotation frequency estimation and NURD correction for IVOCT image data. (Suppl1) International Journal of CARS'2020. 15 (1), 162-163. [Abstract] [doi] [BibTex]
  
  
• N. Gessert (2020). Deep learning with multi-dimensional medical image data. TUHH Open Research: Hamburg, Germany [Abstract] [doi] [www] [BibTex]
  
  
• J. Krüger, R. Opfer, N. Gessert, A.-C. Ostwaldt, P. Manogaran, H. H. Kitzler, A. Schlaefer, S. Schippling (2020). Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. NeuroImage: Clinical. 28 102445. [Abstract] [doi] [www] [BibTex]
  
  
• M. Seemann, L. Bargsten, A. Schlaefer (2020). Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation. Current Directions in Biomedical Engineering. 6 (1), 20200015. [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs, N. Gessert, W. Laffers, D. Eggert, S. Westermann, N.A. Mueller, A.O.H. Gerstners, C. Betz, A. Schlaefer (2020). Spectral-spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification. Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 Springer International Publishing: Cham 690-699. [Abstract] [BibTex]
  
  
• M. Bengs, T. Gessert, A. Schlaefer (2020). 4D spatio-temporal convolutional networks for object position estimation in OCT volumes. Current directions in biomedical engineering. 6 (1), 20200001. [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, F. Siebert, A. Schlaefer (2020). BReP‐SNAP‐T‐54: Efficient Stochastic Optimization Accounting for Uncertainty in HDR Prostate Brachytherapy Needle Placement. Medical Physics. 47 (6), e458. [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, C. Fürweger, T. Hofmann, A. Schlaefer (2020). Multicriterial CNN based beam generation for robotic radiosurgery of the prostate. Current Directions in Biomedical Engineering. 6 (1), 20200030. [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, C. Fürweger, T. Hofmann, A. Schlaefer (2020). Feasibility and analysis of CNN-based candidate beam generation for robotic radiosurgery. Medical Physics. 47 (9), 3806-3815. [Abstract] [doi] [www] [BibTex]
  
  
• F. Behrendt, N. Gessert, A. Schlaefer (2020). Generalization of spatio-temporal deep learning for vision-based force estimation. Current Directions in Biomedical Engineering. 6 (1), 20200024. [Abstract] [doi] [www] [BibTex]
  
  
• M. Gromniak, M. Neidhardt, A. Heinemann, K. Püschel, A. Schlaefer (2020). Needle placement accuracy in CT-guided robotic post mortem biopsy. Current Directions in Biomedical Engineering. 6 (1), 20200031. [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt, N. Gessert, T. Gosau, J. Kemmling, S. Feldhaus, U. Schumacher, A. Schlaefer (2020). Force estimation from 4D OCT data in a human tumor xenograft mouse model. Current Directions in Biomedical Engineering. 6 (1), 20200022. [Abstract] [doi] [www] [BibTex]
  
  
• L. Bargsten, A. Schlaefer (2020). SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. International Journal of Computer Assisted Radiology and Surgery. 15 (9), 1427-1436. [Abstract] [doi] [www] [BibTex]
  
  
• N. Gessert, J. Krüger, R. Opfer, A.-C. Ostwaldt, P. Manogaran, H. H. Kitzler, S. Schippling, A. Schlaefer (2020). Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs. Computerized Medical Imaging and Graphics. 84 (101772), [Abstract] [doi] [www] [BibTex]
  
  
• M. Gromniak, N. Gessert, T. Saathoff, A. Schlaefer (2020). Needle tip force estimation by deep learning from raw spectral OCT data. International Journal of Computer Assisted Radiology and Surgery. 15 1699-1702. [Abstract] [doi] [www] [BibTex]
  
  
• N. Gessert, M. Bengs, M. Schlüter, A. Schlaefer (2020). Deep learning with 4D spatio-temporal data representations for OCT-based force estimation. Medical Image Analysis. 64 (101730), [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and N. Gessert and M. Schlüter and A. Schlaefer (2020). Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data. International Journal of Computer Assisted Radiology and Surgery. 15 (6), 943-952. [Abstract] [doi] [www] [BibTex]
  
  
• M. Schlüter, L. Glandorf, M. Gromniak, T. Saathoff, A. Schlaefer (2020). Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography. Sensors. 20 (9), 2678. [Abstract] [doi] [BibTex]
  
  
• A. Rogalla, S. Lehmann, M. Neidhardt, J. Sprenger, M. Bengs, A. Schlaefer, S. Schupp (2020). Synthesizing Strategies for Needle Steering in Gelatin Phantoms. Models for Formal Analysis of Real Systems (MARS 2020) [Abstract] [doi] [www] [BibTex]
  
  
• N. Gessert, M. Bengs, J. Krüger, R. Opfer, A.-C. Ostwaldt, P. Manogaran, S. Schippling, A. Schlaefer (2020). 4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation. Medical Imaging with Deep Learning [Abstract] [www] [BibTex]
  
  
• N. Gessert, M. Nielsen, M. Shaikh, R. Werner, A. Schlaefer (2020). Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX. 7 100864. [Abstract] [doi] [www] [BibTex]
  ISIC Skin Lesion Classification Challenge @ MICCAI 2019. [method][Challenge] First place in both challenge tasks.
  
  
• F. Griese, S. Latus, M. Schlüter, M. Graeser, M. Lutz, A. Schlaefer, T. Knopp (2020). In-Vitro MPI-guided IVOCT catheter tracking in real time for motion artifact compensation. PLOS ONE. 15 (3), e0230821. [Abstract] [doi] [www] [BibTex]
  
  
• S. Latus, P. Breitfeld, M. Neidhardt, W. Reip, C. Zöllner, A. Schlaefer (2020). Boundary prediction during epidural punctures based on OCT relative motion analysis. EUR J ANAESTH. 2020 (Volume 37 | e-Supplement 58 | June 2020), [Abstract] [BibTex]
  
  
• D.B. Ellebrecht, S. Latus, A. Schlaefer, T. Keck, N. Gessert (2020). Towards an Optical Biopsy during Visceral Surgical Interventions. Visceral Medicine. 36 (2), 70–79. [Abstract] [doi] [BibTex]
  
  
• M. Bengs, N. Gessert, A. Schlaefer (2020). A Deep Learning Approach for Motion Forecasting Using 4D OCT Data. International Conference on Medical Imaging with Deep Learning [Abstract] [www] [BibTex]
  
  
• M. Neidhardt, M. Bengs, S. Latus, M. Schlüter, T. Saathoff, A. Schlaefer (2020). 4D Deep learning for real-time volumetric optical coherence elastography. International Journal of Computer Assisted Radiology and Surgery 2020 1861-6429. [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs, S. Westermann, N. Gessert, D. Eggert, A. O. H. Gerstner, N. A. Mueller, C. Betz, W. Laffers, A. Schlaefer (2020). Spatio-spectral deep learning methods for in-vivohyperspectral laryngeal cancer detection. SPIE Medical Imaging 2020: Computer-Aided Diagnosis. in print. [BibTex]
  
  
• M. Neidhardt, M. Bengs, S. Latus, M. Schlüter, T. Saathoff, A. Schlaefer (2020). Deep Learning for High Speed Optical Coherence Elastography. IEEE International Symposium on Biomedical Imaging 1583-1586. [Abstract] [doi] [BibTex]
  
  
• M. Schlüter, L. Glandorf, J. Sprenger, M. Gromniak, M. Neidhardt, T. Saathoff, A. Schlaefer (2020). High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images. IEEE International Symposium on Biomedical Imaging 1979-1982. [Abstract] [doi] [BibTex]
  
  
• N. Gessert, T. Sentker, F. Madesta, R. Schmitz, H. Kniep, I. Baltruschat, R. Werner, A. Schlaefer (2020). Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting. IEEE Transactions on Biomedical Engineering. 67 (2), 495-503. [Abstract] [doi] [www] [BibTex]
  
  
• N. Gessert, A. Schlaefer (2020). Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM@MICCAI 2019. Lecture Notes in Computer Science. 375-383. [Abstract] [www] [BibTex]
  
  
• N. Gessert, M. Bengs, A. Schlaefer (2020). Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models. SPIE Medical Imaging 2020: Computer-Aided Diagnosis. 11314 1131414. [Abstract] [www] [BibTex]