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Florian Thamm, M. Sc.

Researcher

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

Room: Room 09.158
Martensstr. 3
91058 Erlangen

Office hours

Each week Mo, Tu,

 

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 

A 1minute Teaser 😉

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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

2020

Conference Contributions

2019

Book Contributions

Conference Contributions

Übung (UE)

  • 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
    • Mon 12:00-14:00, Room 0.01-142 CIP
    • Tue 18:00-20:00, Room 0.01-142 CIP
    • Thu 14:00-16:00, Room 0.01-142 CIP
    • Fri 8:00-10:00, Room 0.01-142 CIP
  • 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
    • Tue 18:00-20:00, Room 0.01-142 CIP
    • Mon 12:00-14:00, Room 0.01-142 CIP

Vorlesung (VORL)

  • Deep Learning

    Information regarding the online teaching will be added to the studon course

    • Tue 16:15-17:45, Room H4
  • Deep Learning

    Information regarding the online teaching will be added to the studon course

    • Tue 16:15-17:45, Room H4