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2020

  • L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, J. Sprenger, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer (2020). Tailored Methods for Segmentation of Intravascular Ultrasound Images via Convolutional Neural Networks. SPIE Medical Imaging Conference accepted. [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, 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 accepted. [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 accepted. [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]