Detail

Keywords

Image Processing, Computer-Assisted; Medical Physics; Neural Networks (Computer); Optical Imaging; Physics; Tomography, Optical Coherence

Research group(s)

• Werkmeister Group
  Head: Rene Werkmeister
  Research Area: We are working on the identification and quantification of novel OCT imaging biomarkers.
  Members:
  Bhavapriya Jasmin Schäfer
  Rene Werkmeister
  Kornelia Schützenberger
  Hannes Stegmann
  Alina Messner
  Martin Pfister

Research interests

My research focus is on Optical Coherence Tomography (OCT) and its application in ophthalmology and dermatology.

Selected publications

1. Pfister, M. et al., 2021. Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images. Annals of the New York Academy of Sciences. Available at: http://dx.doi.org/10.1111/nyas.14582.
2. Pfister, M. et al., 2021. Optical Coherence Tomography Angiography Monitors Cutaneous Wound Healing under Angiogenesis-Promoting Treatment in Diabetic and Non-Diabetic Mice. Applied Sciences, 11(5), p.2447. Available at: http://dx.doi.org/10.3390/app11052447.
3. Santos, V.A. dos et al., 2019. CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomedical Optics Express, 10(2), p.622. Available at: http://dx.doi.org/10.1364/boe.10.000622.
4. Pfister, M. et al., 2019. Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks. Biomedical Optics Express, 10(3), p.1315. Available at: http://dx.doi.org/10.1364/BOE.10.001315.
5. Stegmann, H. et al., 2020. Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus. Biomedical Optics Express, 11(3), p.1539. Available at: http://dx.doi.org/10.1364/BOE.386228.