Nils Gessert

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 Research Assistent

 +49 (0)40 42878 3389

  E.3083

  nils.gesserttuhh.de

  Google Scholar Profile

 

2017 - today MTEC Institute / TUHH
2014 - 2016 Northern Institute of Technology Management
2011 - 2017 Hamburg University of Technology

Interests

  • Deep Learning for Medical Image Analysis
  • 3D CNN Architectures for Optical Coherence Tomography (OCT) Data
  • OCT-based Tracking, Force Estimation and Disease Classification

Roles

  • Research assistant
  • PhD student

Publications

2018

  • N. Gessert, M. Lutz, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, A. Schlaefer (2018). Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging. Accepted. [arxiv][doi]

  • N. Gessert, T. Sentker, F. Madesta, R. Schmitz, H. Kniep, I. Baltruschat, R. Werner, A. Schlaefer (2018). Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting. ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018. [arxiv][ISIC] Oral. Best challenge submission with public data only. Overall 2nd placed team.

  • O. Rajput*, N. Gessert*, M. Gromniak, L. Matthäus, A. Schlaefer (2018). Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks. CURAC 2018 conference proceedings. Accepted. [arxiv] *Shared First Authors

  • N. Gessert, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, M. Lutz, A. Schlaefer (2018). Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification. International Conference on Medical Imaging with Deep Learning [Abstract] [arxiv] [openReview] [BibTex]

  • N. Gessert, T. Priegnitz, T. Saathoff, S.-T. Antoni, D. Meyer, M. F. Hamann, K.-P. Jünemann, C. Otte, A. Schlaefer (2018). Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture. International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2018. 11073, 222-229. [Abstract] [arxiv] [BibTex][doi]

  • N. Gessert, J. Beringhoff, C. Otte, A. Schlaefer (2018). Force Estimation from OCT Volumes using 3D CNNs. Int J CARS. 13(7), 1073–1082. [arxiv][doi][BibTex]

  • N. Gessert, M. Heyder, S. Latus, M. Lutz, A. Schlaefer (2018). Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning. Int J CARS. 13(Suppl 1), 99-100. [arxiv][doi]

  • N. Gessert, M. Schlüter, A. Schlaefer (2018). A Deep Learning Approach for Pose Estimation from Volumetric OCT Data. Medical Image Analysis. 46, 162-179. [arxiv][doi] [BibTex]

  • S. Latus, F. Griese, M. Gräser, M. Möddel, M. Schlüter, C. Otte, N. Gessert, T. Saathoff, T. Knopp, A. Schlaefer (2018). Towards bimodal intravascular OCT MPI volumetric imaging. Medical Imaging 2018: Physics of Medical Imaging 105732E. [doi] [BibTex]

2017

  • K. V. Laino, T. Saathoff, T. R. Savarimuthu, K. Lindberg Schwaner, N. Gessert, A. Schlaefer (2017). Design and implementation of a wireless instrument adapter. 16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie 31-36. [arxiv][BibTex]

  • O. Rajput, M. Schlüter, N. Gessert, T. R. Savarimuthu, C. Otte, S.-T. Antoni, A. Schlaefer (2017). Robotic OCT Volume Acquisition Using a Single Fiber. 16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie 232-233. [BibTex]

Projects

OCT_principle.png

The principle of Optical Coherence Tomography (OCT). The infrared light-based imaging modality allows for volumetric depth scans with micrometer level resolution. The imaging modality is used in most of my projects.

general_setup_force_vol.png

Application example: Visual deformation of tissue in C-Scans can be used for force estimation. This allows for sensorless haptic feedback in teleoperation.

tracking_example.png

Application example: C-Scans are volumetric data which requires 3D CNN architectures, e.g. for tracking. Thousands of OCT volumes are acquired and labeld with robot poses. The trained CNN can infer the marker's pose from an acquired volume.

combined.png

Application example: Intravascular OCT (IVOCT) can be used to detect plaque deposits in coronary arteries. Here, a 2D CNN learns to classify plaque from 2D IVOCT slices. In the image, the slices are stacked together such that they show a section of the artery. Green indicates that no plaque was detected, red indicates that plaque was detected.

Member of programm committees, editorial boards and reviewer activities

Student Theses

In general, I supervise student theses with topics on deep learning for medical problems. In case you are interested in this field, contact me via E-Mail. 

Requirements:

  • Very good academic record
  • Very good programming skills (e.g. Python/Java/C/C++)
  • Background knowledge on machine learning and deep learning
  • Motivation and the ability to think critically and work independently

Please provide in your E-Mail:

  • Bachelor certificate and current transcript of records
  • Curriculum Vitae
  • A short text where you highlight your technical interests and related prior experiences