2024

• F. Behrendt and D. Bhattacharya and L. Maack and J. Krüger and R. Opfer and R. Mieling and A. Schlaefer (2024). Diffusion models with ensembled structure-based anomaly scoring for unsupervised anomaly detection. IEEE International Symposion on Biomedical Imaging (ISBI) Accepted [BibTex]
  
  
• F. Behrendt and D. Bhattacharya and L. Maack and J. Krüger and R. Opfer and A. Schlaefer (2024). Combining Reconstruction-based Unsupervised Anomaly Detection with Supervised Segmentation for Brain {MRI}s. Submitted to Medical Imaging with Deep Learning Accepted [Abstract] [www] [BibTex]
  
  
• F. Behrendt and S. Sonawane and D. Bhattacharya and L. Maack and J. Krüger and R. Opfer and A. Schlaefer (2024). Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation. Medical Imaging 2024: Computer-Aided Diagnosis SPIE Accepted [BibTex]
  
  
• M. Neidhardt and R. Mieling and S. Latus and M. Fischer and T. Maurer and A. Schlaefer (2024). A Modified da Vinci Surgical Instrument for OCE based Elasticity Estimation with Deep Learning. Proceedings of the 10th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 1024) Accepted for presentation [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt and S. Latus and L. Maack and S. Gerlach and F. von Brackel and B. Busse and A. Schlaefer (2024). VR-based Body Tracking for Homecare Training. Proceedings of the Smart Healthy Environments Converence International Conference 2024 Accepted for presentation [BibTex]
  
  
• S. Gerlach and F.-A. Siebert and A. Schlaefer (2024). Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy. Medical Physics. 51. (1), 464-475 [Abstract] [doi] [www] [BibTex]
  
  
• S. Latus and M. Kulas and J. Sprenger and D. Bhattacharya and P. C. Breda and L. Wittig and T. Eixmann and G. Hütmann and L. Maack and D. Eggert and C. Betz and A. Schlaefer (2024). Motion-compensated OCT imaging of laryngeal tissue.. Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling SPIE. Accepted [Abstract] [BibTex]
  
  
• F. Behrendt and D. Bhattacharya and J. Krüger and R. Opfer and A. Schlaefer (2024). Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI. In Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit (Eds.) Medical Imaging with Deep Learning PMLR: 1019-1032 [Abstract] [www] [BibTex]
  
  

2023

• Bengs, Marcel (2023). Spatio-temporal deep learning for medical image sequences. [Abstract] [doi] [www] [BibTex]
  
  
• C. Stapper and S. Gerlach and T. Hofmann and C. Füweger and A. Schlaefer (2023). Automated isocenter optimization approach for treatment planning for gyroscopic radiosurgery. Medical Physics. 50. (8), 5212-5221 [Abstract] [doi] [www] [BibTex]
  
  
• S. Grube and M. Bengs and M. Neidhardt and S. Latus and A. Schlaefer (2023). Ultrasound shear wave velocity estimation in a small field of view via spatio-temporal deep learning. In Olivier Colliot and Ivana Išgum (Eds.) Medical Imaging 2023: Image Processing SPIE: 1246425 [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach, T. Hofmann, C. Fürweger, A. Schlaefer (2023). Towards fast adaptive replanning by constrained reoptimization for intra-fractional non-periodic motion during robotic SBRT. Medical Physics. 50. (7), 4613-4622 [Abstract] [doi] [www] [BibTex]
  
  
• S. A. Hoffmann and D. Bhattacharya and B. Becker and D. Beyersdorff and E. Petersen and M. Petersen and D. Eggert and A. Schläfer and C. Betz (2023). Analysing the feasibility of an automated AI-based classifier for detecting paranasal anomalies in the maxillary sinus. 102. (S 02), [Abstract] [doi] [www] [BibTex]
  
  
• S. A. Hoffmann and D. Bhattacharya and B. Becker and D. Beyersdorff and E. Petersen and M. Petersen and D. Eggert and A. Schläfer and C. Betz (2023). Machbarkeitsanalyse eines automatisierten KI-basierten Klassifikationssystems zur Erkennung von Kieferhöhlenbefunden. Laryngo-Rhino-Otologie. 102. (S 02), [Abstract] [doi] [www] [BibTex]
  
  
• R. Mieling and S. Latus and M. Fischer and F. Behrendt and A. Schlaefer (2023). Optical Coherence Elastography Needle for Biomechanical Characterization of Deep Tissue. In Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell (Eds.) Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023 Springer Nature Switzerland: Cham 607-617 [Abstract] [doi] [BibTex]
  
  
• M. Stender and J. Ohlsen and H. Geisler and A. Chabchoub and N. Hoffmann and A. Schlaefer (2023). Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics. Computational Mechanics. 71. (6), 1227-1249 [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt and S. Gerlach F. N. Schmidt and I. A. K. Fiedler and S. Grube and B. Busse and A. Schlaefer (2023). VR-based body tracking to stimulate musculoskeletal training. [BibTex]
  
  
• M. Neidhardt and S. Gerlach F. N. Schmidt and I. A. K. Fiedler and S. Grube and B. Busse and A. Schlaefer (2023). VR-based body tracking to stimulate musculoskeletal training. CURAC 2023 Tagungsband Inprint [Abstract] [www] [BibTex]
  
  
• M. Neidhardt and R. Mieling and M. Bengs and A. Schlaefer (2023). Optical force estimation for interactions between tool and soft tissues. Scientific Reports. 13. (1), 506 [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and J. Sprenger and S. Gerlach and M. Neidhardt and A. Schlaefer (2023). Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy. IEEE Transactions on Biomedical Engineering. 1-10 [Abstract] [doi] [BibTex]
  
  
• I. Kniep and R. Mieling and M. Gerling and A. Schlaefer and A. Heinemann and B. Ondruschka (2023). Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context. Journal of Imaging. 9. (9), [Abstract] [doi] [www] [BibTex]
  
  
• Finn Behrendt and Debayan Bhattacharya and Julia Krüger and Roland Opfer and Alexander Schlaefer (2023). Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI. [Abstract] [BibTex]
  
  
• F. Behrendt and M. Bengs and D. Bhattacharya and J. Krüger and R. Opfer and A. Schlaefer (2023). A systematic approach to deep learning-based nodule detection in chest radiographs. Scientific Reports. 13. (1), 10120 [Abstract] [doi] [www] [BibTex]
  
  
• D. Eggert and D. Bhattacharya and A. Felicio-Briegel and V. Volgger and A. Schlaefer and C. Betz (2023). Deep-Learning-basierte Aufnahmeunterstützung für endoskopisches Narrow Band Imaging des Larynx. 102. (S 02), [Abstract] [doi] [www] [BibTex]
  
  
• D. Eggert and D. Bhattacharya and A. Felicio-Briegel and V. Volgger and A. Schlaefer and C. Betz (2023). Deep-learning-based image acquisition support tool for endoscopic narrow Band Imaging of the Larynx. 102. (S 02), [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya and S. Latus and F. Behrendt and F. Thimm and D. Eggert and C. Betz and A. Schlaefer (2023). Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography. Accepted [Abstract] [www] [BibTex]
  
  
• D. Bhattacharya and F. Behrendt and B. T. Becker and D. Beyersdorff and E. Petersen and M. Petersen and B. Cheng and D. Eggert and C. Betz and A. S. Hoffmann and A. Schlaefer (2023). Unsupervised anomaly detection of paranasal anomalies in the maxillary sinus. In Khan M. Iftekharuddin and Weijie Chen (Eds.) Medical Imaging 2023: Computer-Aided Diagnosis SPIE: 124651B [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya and F. Behrendt and B. T. Becker and D. Beyersdorff and E. Petersen and M. Petersen and B. Cheng and D. Eggert and C. Betz and A. S. Hoffmann and A. Schlaefer (2023). Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus. International Journal of Computer Assisted Radiology and Surgery. [Abstract] [doi] [www] [BibTex]
  
  
• S. Kolibová and E. Wölfel and H. Hemmatian and P. Milovanovic and H. Mushumba and B. Wulff and M. Neidhardt and K. Püschel and A. Failla and A. Vlug and A. Schlaefer and B. Ondruschka and M. Amling and L. Hofbauer and M. Rauner and B. Busse and K. Jähn-Rickert (2023). Osteocyte apoptosis and cellular micropetrosis signify skeletal aging in type 1 diabetes. Acta Biomaterialia. [Abstract] [doi] [BibTex] [pmid]
  
  
• F. Behrendt and D. Bhattacharya and J. Krüger and R. Roland and A. Schlaefer (2023). Nodule Detection in Chest Radiographs with Unsupervised Pre-Trained Detection Transformers. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) 1-4 [Abstract] [doi] [BibTex]
  
  
• R. Mieling and M. Neidhardt and S. Latus and C. Stapper and S. Gerlach and I. Kniep and A. Heinemann and B. Ondruschka and A. Schlaefer (2023). Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip Force Feedback. 2023 IEEE International Conference on Robotics and Automation (ICRA) 6893-6900 [Abstract] [doi] [BibTex]
  
  
• S. Latus and S. Grube and T. Eixmann and M. Neidhardt and S. Gerlach and R. Mieling and G. Hüttmann and M. Lutz and A. Schlaefer (2023). A Miniature Dual-Fiber Probe for Quantitative Optical Coherence Elastography. IEEE Transactions on Biomedical Engineering. 70. (11), 3064-3072 [Abstract] [doi] [BibTex]
  
  

2022

• D. Bhattacharya and B. T. Becker and F. Behrendt and M. Bengs and D. Beyersdorff and D. Eggert and E. Petersen and F. Jansen and M. Petersen and B. Cheng and C. Betz and A. Schlaefer and A. S. Hoffmann (2022). Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus. In Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo (Eds.) Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022 Springer Nature Switzerland: Cham 429-438 [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya and D. Eggert and C. Betz and A. Schlaefer (2022). Squeeze and multi-context attention for polyp segmentation. International Journal of Imaging Systems and Technology. n/a. (n/a), [Abstract] [doi] [www] [BibTex]
  
  
• S. Lehmann, A. Rogalla, M. Neidhardt, A. Reinecke, A. Schlaefer, S. Schupp (2022). Modeling R³ Needle Steering in Uppaal. In Dubslaff, Clemens and Luttik, Bas (Eds.) {\rm Proceedings Fifth Workshop on} Models for Formal Analysis of Real Systems, {\rm Munich, Germany, 2nd April 2022} Open Publishing Association: 40-59 [Abstract] [doi] [BibTex]
  
  
• S. Grube and M. Neidhardt and S. Latus and 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]
  
  
• S. Gerlach and T. Hofmann and C. Fürweger and A. Schlaefer (2022). AI-based optimization for US-guided radiation therapy of the prostate. International Journal of Computer Assisted Radiology and Surgery. [Abstract] [doi] [www] [BibTex]
  
  
• S. Gerlach and T. Hofmann and C. Fuerweger and 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]
  
  
• S. Gerlach and A. Schlaefer (2022). Robotic Systems in Radiotherapy and Radiosurgery. Current Robotics Reports. [Abstract] [doi] [www] [BibTex]
  
  
• R. Mieling and C. Stapper and S. Gerlach and M. Neidhardt and S. Latus and M. Gromniak and P. Breitfeld and 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]
  
  
• M. Neidhardt and M. Bengs and S. Latus and S. Gerlach and C. J. Cyron and J. Sprenger and A. Schlaefer (2022). Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning. IEEE Transactions on Biomedical Engineering. 1-1 [Abstract] [doi] [www] [BibTex]
  
  
• M. H. Laves and M. Tölle and A. Schlaefer and S. Engelhardt (2022). Posterior temperature optimized Bayesian models for inverse problems in medical imaging. Medical Image Analysis. 78. 102382 [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and F. Behrendt and M.-H. Laves and J. Krüger and R. Opfer and 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]
  
  
• L. Maack and L. Holstein and 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]
  
  
• J. Sprenger and M. Neidhardt and S. Latus and S. Grube and M. Fischer and 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]
  
  
• J. Sprenger and M. Bengs and S. Gerlach and M. Neidhardt and A. Schlaefer (2022). Systematic analysis of volumetric ultrasound parameters for markerless 4D motion tracking. International Journal of Computer Assisted Radiology and Surgery. [Abstract] [doi] [www] [BibTex]
  
  
• Gerlach, Stefan and Schlaefer, Alexander (2022). Robotic Systems in Radiotherapy and Radiosurgery. Current Robotics Reports. [Abstract] [doi] [www] [BibTex]
  
  
• F. Behrendt and M. Bengs and F. Rogge and J. Krüger and R. Opfer and 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]
  
  
• F. Behrendt and M. Bengs and D. Bhattacharya and J. Krüger and R. Opfer and A. Schlaefer (2022). Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly Detection. Medical Imaging with Deep Learning. [Abstract] [www] [BibTex]
  
  
• F. Behrendt and D. Bhattacharya and J. Krüger and R. Opfer and 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]
  
  
• D. Eggert and M. Bengs and S. Westermann and N. Gessert and A. O. H. Gerstner and N. A. Mueller and J. Bewarder and A. Schlaefer and C. Betz, and 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]
  
  
• D. Bhattacharya and F. Behrendt and A. Felicio-Briegel and V. Volgger and D. Eggert and C. Betz and 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]
  
  
• T. Sonntag and M. Bauer and J. Sprenger and S. Gerlach and P. Breitfeld and 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]
  
  
• M. Neidhardt and S. Gerlach and R. Mieling and M.-H. Laves and T. Weiß, and M. Gromniak and A. Fitzek and D. Möbius and I. Kniep and A. Ron and J. Schädler and A. Heinemann K. and Püschel and B. Ondruschka and 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]
  
  

2021

• D. B. Ellebrecht and N. Hessler and A. Schlaefer and N. Gessert (2021). Confocal Laser Microscopy for in vivo Intraoperative Application: Diagnostic Accuracy of Investigator and Machine Learning Strategies. [Abstract] [doi] [www] [BibTex]
  
  
• D. Bhattacharya and C. Betz and D. Eggert and A. Schlaefer (2021). Self-Supervised U-Net for Segmenting Flat and Sessile Polyps.. SPIE Medical Imaging Symposium 2021 [Abstract] [www] [BibTex]
  
  
• S. Gerlach and M. Neidhardt and T. Weiß and M.-H. Laves and C. Stapper and M. Gromniak and I. Kniep and D. Möbius and A. Heinemann and B. Ondruschka and 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]
  
  
• R. Mieling and J. Sprenger and S. Latus and L. Bargsten and 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. Schlüter (2021). Analysis of ultrasound and optical coherence tomography for markerless volumetric image guidance in robotic radiosurgery. [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt and S. Gerlach and M.-H. Laves and S. Latus and C. Stapper and M. Gromniak and A. Schlaefer (2021). Collaborative robot assisted smart needle placement. Current Directions in Biomedical Engineering. 7. (2), 472--475 [Abstract] [doi] [www] [BibTex]
  
  
• M. Neidhardt and J. Ohlsen and N. Hoffmann and A. Schlaefer (2021). Parameter Identification for Ultrasound Shear Wave Elastography Simulation:. Current Directions in Biomedical Engineering. 7. (1), 35-38 [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and S. Pant and M. Bockmayr and U. Schüller and 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]
  
  
• M. Bengs and M. Bockmayr and U. Schüller and 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]
  
  
• M. Bengs and F. Behrendt and J. Krüger and R. Opfer and 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 and F. Behrendt and J. Krüger and R. Opfer and 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] [BibTex]
  
  
• L. Bargsten and S. Raschka and 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]
  
  
• L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and 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]
  
  
• L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and J. Sprenger and M. Grass and M. Seiffert and S. Blankenberg and 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]
  
  
• L. Bargsten and D. Klisch and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and 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]
  
  
• K. P. Abdolazizi and K. Linka and J. Sprenger and M. Neidhardt and A. Schlaefer and C. J. Cyron (2021). Identification of the concentration‐dependent viscoelastic constitutive parameters of gelatin by combining computational mechanics and machine learning. Proceedings in applied mathematics and mechanics. 21. (1), e202100250 [Abstract] [www] [BibTex]
  
  
• K. P. Abdolazizi and K. Linka and J. Sprenger and M. Neidhardt and A. Schlaefer and C. J. Cyron (2021). Concentration-Specific Constitutive Modeling of Gelatin Based on Artificial Neural Networks. PAMM. 20. (1), e202000284 [Abstract] [doi] [www] [BibTex]
  
  
• K. Linka and M. Hillgärtner and K. P. Abdolazizi and R. C. Aydin and M. Itskov and C. J. Cyron (2021). Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning. Journal of Computational Physics. 429. 110010 [Abstract] [doi] [www] [BibTex]
  
  
• J. Sprenger and T. Saathoff and A. Schlaefer (2021). Automated robotic surface scanning with optical coherence tomography. IEEE 18th International Symposium on Biomedical Imaging 1137-1140 [Abstract] [BibTex]
  
  
• J. Sprenger and M. Neidhardt and M. Schlüter and S. Latus and T. Gosau and J. Kemmling and S. Feldhaus and U. Schumacher and 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 and J. Petersen and N. Neumann and H. Reichenspurner and D. Russ and C. Detter and 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]
  
  
• J. Ohlsen and M. Neidhardt and A. Schlaefer and 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]
  
  
• J. Krüger and A. C. Ostwaldt and L. Spies and B. Geisler and A. Schlaefer, and H. H. Kitzler and S. Schippling and 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]
  
  
• J. F. Fast and H. R. Dava and A. K. Rüppel and D. Kundrat and M. Krauth and M.-H. Laves and S. Spindeldreier and L. A. Kahrs and 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]
  
  
• G. A. Holzapfel and K. Linka and S. Sherifova and C. J. Cyron (2021). Predictive constitutive modelling of arteries by deep learning. Journal of The Royal Society Interface. 18. (182), 20210411 [Abstract] [doi] [www] [BibTex]
  
  
• F. N. Schmidt and S. Gerlach and M. Issleib and A. Schlaefer and 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]
  
  
• D. Bhattacharya and C. Betz and D. Eggert and 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]
  
  
• L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and 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]
  
  
• S. Lehmann, and A. Rogalla and M. Neidhardt and A. Schlaefer S. and 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]
  
  
• S. Latus and J. Sprenger and M. Neidhardt and J. Schadler and A. Ron and A. Fitzek and M. Schlüter and P. Breitfeld and A. Heinemann and K. Püschel and 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]
  
  

2020

• A. Rogalla and S. Lehmann and M. Neidhardt and J. Sprenger and M. Bengs and A. Schlaefer and S. Schupp (2020). Synthesizing Strategies for Needle Steering in Gelatin Phantoms. Models for Formal Analysis of Real Systems (MARS 2020) [Abstract] [doi] [www] [BibTex]
  
  
• D.B. Ellebrecht and S. Latus and A. Schlaefer and T. Keck and N. Gessert (2020). Towards an Optical Biopsy during Visceral Surgical Interventions. Visceral Medicine. [Abstract] [doi] [BibTex]
  
  
• J. Krüger and R. Opfer and N. Gessert and A.-C. Ostwaldt and P. Manogaran and H. H. Kitzler and A. Schlaefer and 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. Bengs and N. Gessert and A. Schlaefer (2020). A Deep Learning Approach for Motion Forecasting Using 4D OCT Data. International Conference on Medical Imaging with Deep Learning 2004.10121 [Abstract] [www] [BibTex]
  
  
• M. Bengs and N. Gessert and A. Schlaefer (2020). 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification. arXiv: [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and N. Gessert and W. Laffers and D. Eggert and S. Westermann and N.A. Mueller and A.O.H. Gerstners and C. Betz and 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 and S. Westermann and N. Gessert and D. Eggert and A. O. H. Gerstner and N. A. Mueller and C. Betz and W. Laffers and A. Schlaefer (2020). Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection. In Horst K. Hahn and Maciej A. Mazurowski (Eds.) Medical Imaging 2020: Computer-Aided Diagnosis SPIE: 113141L [Abstract] [doi] [www] [BibTex]
  
  
• M. Bengs and S. Westermann and N. Gessert and D. Eggert and A. O. H. Gerstner, N. A. Mueller and C. Betz and W. Laffers and 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. Bengs and T. Gessert and 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]
  
  
• M. Gromniak and N. Gessert and T. Saathoff and 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]
  
  
• F. Griese AND S. Latus AND M. Schlüter AND M. Graeser AND M. Lutz AND A. Schlaefer AND 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]
  
  
• 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]
  
  
• F. Behrendt and N. Gessert and 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 and M. Neidhardt and A. Heinemann and K. Püschel and 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]
  
  
• L. Bargsten and 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]
  
  
• A. Rogalla and T. Kamph and U. Bulmann and K. Billerbeck and M. Blumreiter and S. Schupp (2020). Designing And Analyzing Open Application-Oriented Labs in Software-Verification Education. Annual Conference of European Society for Engineering Education (SEFI). Enschede (the Netherlands) 444-453 [Abstract] [BibTex]