Florian Thamm, M. Sc.
VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery, Posterior Cerebral Artery left and right) and detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).
https://diglib.eg.org/bitstream/handle/10.2312/vcbm20201181/151-155.pdf
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- Since 11/2019
Ph.D Student at Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg - 04/2017 – 10/2019
M.Sc. Computer Science, 1.1, Friedrich-Alexander-Universität Erlangen-Nürnberg - 10/2013 – 03/2017
B.Eng. Medical Engineering, 1.4, Technische Hochschule Nürnberg
Siemens Healthcare GmbH, Forchheim
- Since 11/2019
Ph.D. Student
CT R&D CTC SA - 03/2019 – 09/2019
Master’s Thesis Student
CT R&D CTC SA - 03/2017 – 09/2019
Working Student
CT R&D APP ALG - 10/2016 – 04/2017
Bachelor’s Thesis Student
AT R&D APP RH - 03/2016 – 10/2016
Working Student
AT R&D APP REC - 10/2015 – 03/2016
Intern
AT R&D APP REC
Friedrich-Alexander-University
- Since 01/2020
Research Assistant for CTA Image Analysis
Exercise Instructor of the Deep Learning Course - 10/2018 – 12/2020
Teaching Assistant, Exercise Instructor of the Deep Learning Course
Technische Hochschule Nürnberg
- 03/2015 – 10/2015
E-Learning Tutor - 10/2014 – 10/2015
Teaching Assistant
2021
Conference Contributions
Abstract: VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data
BVM Workshop 2021 (OTH Regensburg, March 8, 2021 - March 9, 2021)
In: BVM Workshop 2021
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2020
Conference Contributions
VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data
Eurographics Workshop on Visual Computing for Biology and Medicine (Uni Tübingen, September 28, 2020 - October 1, 2020)
In: K. Nieselt and R. G. Raidou (ed.): Eurographics Workshop on Visual Computing for Biology and Medicine 2020
DOI: 10.2312/vcbm.20201181
BibTeX: Download
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RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting
Bilderverarbeitung für die Medizin
Algorithmen - Systeme - Anwendungen (Berlin, March 15, 2020 - March 17, 2020)
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2019
Book Contributions
Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
In: Rainer Röhrig, Harald Binder, Hans-Ulrich Prokosch, Ulrich Sax, Irene Schmidtmann, Susanne Stolpe, Antonia Zapf (ed.): German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, IOS Press, 2019, p. 126-133 (Studies in Health Technology and Informatics, Vol.267)
DOI: 10.3233/SHTI190816
BibTeX: Download
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Conference Contributions
RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting
Medical Image Computing and Computer Assisted Intervention (Shenzhen, October 13, 2019 - October 17, 2019)
In: Proceedings of MICCAI 2019, Cham: 2019
DOI: 10.1007/978-3-030-32248-9_11
BibTeX: Download
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Übung (UE)
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Deep Learning Exercises
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Deep Learning Exercises
This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. Information regarding the online teaching will be added to the studon course
- Wed 16:00-18:00, Room 0.01-142 CIP
- Fri 8:00-10:00, Room 0.01-142 CIP
- Thu 14:00-16:00, Room 0.01-142 CIP
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Deep Learning
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Deep Learning
Information regarding the online teaching will be added to the studon course
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Student | Title | Type | Status |
---|---|---|---|
Annette Schwarz | Real-Time Prospective Respiratory Triggering for Free-Breathing Lung Computed Tomography | MA thesis | running |
Melanie Kienberger | Predictive Maintenance for SINAMICs Frequency Converter | MA thesis | running |
Dmitrij Vinokour | Detection of Hand Drawn Electrical Circuit Diagrams and their Components using Deep Learning Methods and Conversion into LTspice Format | MA thesis | running |
Stephanie Mehltretter | Augmentation of CT Images by Variation of Non-Rigid Deformation Vector Field Amplitudes | Project | running |
Antonia Popp | Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning | MA thesis | running |
Leonhard Rist | Geometric Deep Learning for Multifocal Diseases | MA thesis | finished |
Jiayue Zhao | Optimization of the Input Resolution for Dermoscopy Image Classification Tasks | MA thesis | finished |
Lukas Folle | Classification of Rotator Cuff Tears in MRI using Neural Networks | MA thesis | finished |