Friedrich-Alexander-Universität Erlangen
Lehrstuhl für Mustererkennung
Martensstraße 3
91058 Erlangen
The population modelling group is dedicated to analyse data collected from many individuals in order to be able to model variations within entire populations.
| Responsible Person | Title | |
|---|---|---|
| 08.01.2019 | Mitis | Colloquium Discussion |
| 15.01.2019 | Shuqing Chen | Journal Club - U-Net Variations & Progressions 1: |
| 22.01.2019 | Franziska Kopte | MT Final: Liver Tumor Segmentation Using an Interactive Deep Learning Architecture |
| 29.01.2019 | Theresa Götz | 3D-2D Neural Networks for dose estimation in nuclear medicine |
| 05.02.2019 | Maximilian Reymann Xia Zhong | MT Intro: Denoising in SPECT using Deep Learning JC: Capsules for Object Segmentation ( |
| 12.02.2019 | TBA | |
| 19.02.2019 | TBA | |
| 26.02.2019 | TBA | |
| 05.03.2019 | Philipp Roser | JC:  A Probabilistic U-Net for Segmentation of Ambiguous Images |
| 12.03.2019 | BVM Authors | BVM 2019 Rehearsal Session |
| 19.03.2019 | No colloquium due to BVM | |
| 26.03.2019 | No colloquium due to PRS | |
| 09.04.2019 | Maximilian Reymann | MT Final: Denoising in SPECT using Deep Learning |
| 07.05.2019 | Tobias Schmidt | BT Intro: Evaluation of Augmented Reality Devices for Employee Training using Machine Learning |
| 11.06.2019 | Mohammad Zakeri | MT Intro & Final: Automatic Glottis segmentation from High-Speed video endoscopy of the larynx |
| 25.06.2019 | Harb Alnasser Alabdalrahim | MT Intro: Automatic Evaluation of the Clock-Drawing Dementia Test using Deep Learning |
| 23.07.2019 | Tobias Schmidt | BT Final: Evaluation of Augmented Reality Devices for Employee Training using Machine Learning |
| 13.08.2019 | Harb Alnasser Alabdalrahim | MT Final: Automatic Evaluation of the Clock-Drawing Dementia Test using Deep Learning |
| 20.08.2019 | Sina Ghasemi | MT Final: Head Pose Estimation for Patient Localization Using Deep Neural Network |
Mailing list subscription management page for students and guests.
Prior Sessions 
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X-Ray Imaging Using a Patient ModelThis project focuses on estimate a patient model and model based X-ray imaging application. The current research focus is on anatomical patient modelling and model based skin and scatter estimation. |
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Quantitative DECT with prior atlas knowledgeThis project aims at decomposing the materials with anatomy knowledge automatically. An atlas provides the prior knowledge - the anatomic structures. The anatomy knowledge allows then automatic decomposition and classification of the different materials. |
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Quantification and Respiratory Motion Management for SPECT ImagingPopulation-based metrics for diagnosis and patient stratification are important aids for nuclear medicine physicians when dealing with various cardiological, neurological, and oncological indications. A necessary input to such approaches based on SPECT is a standardized, artifact-free dataset. The goals of this project include improvement of standardization through quantitative imaging and reduction of respiratory motion artifacts through data-driven techniques. |
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Time-of-Flight 4-D Foot Scan3-D Reconstruction of the shape of human feet during motion. |
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Attenuation Correction for Hybrid Imaging SystemsAttenuation maps for correcting PET data needs to be derived from MR information in current PET/MR hybrid systems |
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Catheter Contact ForceCatheter contact force assessment and evaluation for cardiac ablation procedures. |
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Motion CompensationCardiac and respiratory motion compensation for atrial fibrillation ablation procedures. |