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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,

Please do not call me in the office. Write me an E-Mail instead 🙂

 

Can you tell which one is fake?

No? Don’t worry*, radiologists neither can 😉 Check out our most recent work “SyNCCT” published at MICCAI to see how we synthesized CT images from angiography scans.

*solution in the SyNCCT tab below

SyNCCT: Synthetic Non-contrast Images of the Brain from Single-Energy Computed Tomography Angiography

By injecting contrast agent during a CT acquisition, the vascular system can be enhanced. This acquisition type is known as CT Angiography (CTA). However, due to typically lower dose levels of CTA scans compared to non-contrast CT acquisitions (NCCT) and the employed reconstruction designed specifically for vessel reconstruction, soft tissue contrast in the brain parenchyma is usually subpar. Hence, an NCCT scan is preferred for the visualization of such tissue. We propose SyNCCT, an approach which synthesizes NCCT images from the CTA domain by removing enhanced vessel structures and improving soft tissue contrast. Contrary to virtual non-contrast (VNC) images based on dual energy scans, which target the physically accurate removal of iodine rather than generating a realistic NCCT with improved gray/white matter separation, our approach only requires a conventional single-energy acquisition. By design, our method integrates prior domain knowledge and employs residual learning as well as a discriminator to achieve perceptual realism. In our data set of patients with ischemic stroke, the absolute differences in automatic ASPECT scoring, which rates early signs of an occlusion in the anterior circulation on a scale from 0 (most severe) to 10 (no signs), was 0.78 ± 0.75 (median of 1) when comparing our SyNCCT to the real NCCT images. Qualitatively, realistic appearance of the images was confirmed by means of a Turing test with a radiologist, who classified 64% of 64 (32 real, 32 generated) images correctly. Two other physicians classified 65% correctly, on average.

https://www.springerprofessional.de/syncct-synthetic-non-contrast-images-of-the-brain-from-single-en/19692102

And by the way, the left one was fake 😉

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 😉

And here is more Material

Watch the playlist with 3 videos on youtube https://youtu.be/b_IshmarO7E?list=PLJgm15h1F7z5pvrb6qHB9Z0QHbcALefQj

  • 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

2022

Conference Contributions

2021

Conference Contributions

Miscellaneous

2020

Conference Contributions

2019

Book Contributions

Conference Contributions

Student Title Type Status
Estimation of Forming Limit Curves Pattern using Deep Learning Methods MA thesis open
Ankitha Rama Krishna Reddy GAN-based Synthetic Chest X-ray Generation for Training Lung Disease Classification Systems MA thesis running
Benjamin Geißler Interpolation of ARAMIS Grids and Analysis of Numerical Stability on Deep Learning Methods Project running
Dmitrij Vinokour Reinforcement Learning in Finance – Add and adapt the DDQN to an existing Reinforcement Learning Framework Project running
Julian Jendryka Advanced Model Architectures for Interactive Segmentation and Segmentation Enhancement in CT Images MA thesis running
Jonas Schauer Cerebral Vessel Tree Estimation from Non-Contrast CT using Deep Learning Methods MA thesis running
Laurin Schuster Graph Augmentation using Cond.-GANs Project running
Tasnim Nova Post-Processing of DTF-Skeletonizations Project running
Jad Kassam Detection of Large Vessel Occlusions using Graph Deep Learning MA thesis running
Amit Kumar Sharma Prediction of Steam Turbine Blade Vibration Amplitudes using Machine Learning Methods MA thesis finished
Annette Schwarz Real-Time Prospective Respiratory Triggering for Free-Breathing Lung Computed Tomography MA thesis finished
Melanie Kienberger Predictive Maintenance for SINAMICs Frequency Converter MA thesis finished
Dmitrij Vinokour Detection of Hand Drawn Electrical Circuit Diagrams and their Components using Deep Learning Methods and Conversion into LTspice Format MA thesis finished
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 finished
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