Maximilian Reymann - Pattern Recognition Lab

Maximilian Reymann, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room 09.130
Martensstr. 3
91058 Erlangen

Each week Fr,

Since 04/2019:
PhD student at the Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany
In cooperation with Siemens Medical Solutions, Hoffman Estates, USA and Clinic of Nuclear Medicine, University Hospital Erlangen, Germany
Until 03/2019:
Masters degree in Medical Engineering with focus on Image and Data Analytics at the FAU
Until 09/2016:
Bachelors degree in Medical Engineering with focus on Prosthetics at the FAU
03/2016 – 09/2016:
Bachelor’s thesis at the computational Cardiology Lab, Johns Hopkins University, Baltimore, USA

Rao D., Reymann M., Faley P., Massanes F., Gohn W., Vija AH.:
GAN Generated Model Observer for one Class Detection in SPECT Imaging
SPIE (Online, February 15, 2021 - February 19, 2021)
In: SPIE (ed.): GAN Generated Model Observer for one Class Detection in SPECT Imaging 2021
BibTeX: Download

Reymann M., Massanes F., Ritt P., Cachovan M., Kuwert T., Vija AH., Maier A.:
Feature Loss After Denoising of SPECT Projection Data using a U-Net
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (Boston, Massachusetts, October 31, 2020 - November 7, 2020)
In: IEEE (ed.): Feature Loss After Denoising of SPECT Projection Data using a U-Net 2020
BibTeX: Download
Mürschberger N., Reymann M., Cachovan M., Ritt P., Vija H., Maier A.:
U-Net for Multi-Organ Segmentation of SPECT Projection Data
IEEE (Boston, Massachuchets, October 31, 2020 - November 7, 2020)
In: IEEE (ed.): U-Net for Multi-Organ Segmentation of SPECT Projection Data 2020
BibTeX: Download

Reymann M., Würfl T., Ritt P., Cachovan M., Stimpel B., Maier A.:
Deep Image Denoising in SPECT
Annual Congress of the European Association of Nuclear Medicine (Barcelona, October 12, 2019 - October 16, 2019)
In: Springer-Verlag GmbH Germany, part of Springer Nature 2019 (ed.): European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, Berlin, Heidelberg: 2019
DOI: 10.1007/s00259-019-04486-2
URL: https://link.springer.com/article/10.1007/s00259-019-04486-2
BibTeX: Download
Reymann M., Würfl T., Stimpel B., Ritt P., Cachovan M., Vija AH., Maier A.:
U-Net for SPECT Image Denoising
2019 IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC) (Manchester, October 26, 2019 - November 2, 2019)
In: IEEE (ed.): 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) 2019
BibTeX: Download

Type	Title	Status
Project	Manifold Forests	open
Project	Deep Learning based Model Observers for Multi-modal Imaging	finished
Project	Analysis of NVIDIA Optix Engine for Ray Tracing in SPECT	running
Project	GAN Generated Model Observer for one Class Detection in SPECT Imaging	finished
Project	Adapting Pyro-NN with SPECT operators	finished
Project	Advancing the digital twin method	finished
BA thesis	Improved image quality of Limited Angle and Sparse View SPECT using Deep Learning	finished
MA thesis	Synthetic generation of CT image from non-attenuation corrected FDG-PET image using GANs and its application to whole-body PET/CT registration.	running
MA thesis	Convolutional Neural Networks for multi-organ segmentation of SPECT projections	finished