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Julia Schottenhamml, M. Sc.

Researcher

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

Room: Room 10.138
Martensstr. 3
91058 Erlangen

  • 04/2014 – 01/2018:
    Master of Science in Computer Science, Friedrich-Alexander University Erlangen-Nürnberg
  • 10/2010 – 03/2014:
    Bachelor of Science in Medical Engineering, Friedrich-Alexander University Erlangen-Nürnberg

Friedrich-Alexander-University Erlangen-Nürnberg

  • 12/2016 – 03/2017:
    Student research assistant, Digital Sports Group
  • 04/2015 – 10/2015:
    Student teaching assistant, Exercises in Algorithms and Data Structures
  • 06/2014 – 03/2015:
    Student research assistant, Digital Sports Group
  • 04/2013 – 10/2013:
    Student teaching assistant, Exercises in Algorithms and Data Structures
  • 10/2012 – 04/2013:
    Student teaching assistant, Exercises in Signals and Systems 1

Massachusetts Institute of Technology

Forschungs-Neutronenquelle Heinz Maier-Leibnitz (FRMII)

  • 01/2014 – 02/2014:
    Internship, Forschungs-Neutronenquelle Heinz Maier-Leibnitz (FRMII), Munich

  • 2017: MIT Outstanding Poster Award at ARVO 2017
  • 2017: Grant Wood Balkema Memorial Travel Grant

  • Unsupervised OCTA preserving OCT denoising
    Denoising in Optical Coherence Tomography (OCT) and OCT angiography (OCTA) is a very important topic in order to compensate the low signal to noise ratio inherent to that technology. However, the speckle in OCT images consists not only of speckle noise but also of speckle information caused by the movement of blood cells in the vessels. This information is actually used to compute the OCTA images from OCT scans. All existing denoising methods at the moment do not distinguish between speckle noise and speckle information and therefore eradicate the information as well as the speckle. Another drawback is that because of the nature of OCT devices, no ground truth can be generated. At the moment this is solved by acquiring multiple scans an performing an averaging. This has the downside that a very good registration is needed and that scan times are longer, which may cause problems in elderly people or people with certain pathologies that have a hard time focusing. We therefore investigate unsupervised methods to distinguish between speckle information and speckle noise and only remove the latter.
    This project is carried out in collaboration with Prof. Fujimoto’s Biomedical Optical Imaging and Biophotonics Group at MIT in Cambridge, USA and the opthalmic clinic of the university hospital Erlangen.
  • OCT-OCTA Segmentation of the Bruch’s Membrane in the Presence of Pathology:
    Because of the ever increasing size of OCT(A) data sets, manual segmentation of ocular layers is typically impractically laborious, necessitating the usage of automatic algorithms. While automatic segmentation of ocular layers in the normal eye can be challenging, automatic segmentation in the presence of pathology, which often causes significant ocular distortions, is more challenging still.

    Perhaps nowhere is the demand for accurate segmentation more stringent than in OCTA analysis of the choriocapillaris. Analysis of the choriocapillaris is of great interest in studying a number of diseases, including age-related macular degeneration (AMD) and diabetic retinopathy (DR), where the choriocapillaris is thought to play an important role. En face OCTA analysis of the choriocapillaris typically requires segmentation of Bruch’s membrane (BM), an acellular matrix situated between the retinal pigment epithelium (RPE) and choriocapillaris. However, to our knowledge, all previous publications on segmenting the BM have exclusively used OCT data. We think that OCTA data can be used synergistically alongside OCT data to segment Bruch’s membrane, as well as other ocular structures. As such, the scope of this project is to explore different ways of incorporating and exploiting the additional information provided by the OCTA data in order to get more accurate and robust segmentation results for the BM in the presence of pathology.
    This project is carried out in collaboration with Prof. Fujimoto’s Biomedical Optical Imaging and Biophotonics Group at MIT in Cambridge, USA.

2019

  • PPP Brasilien 2019

    (Third Party Funds Single)

    Term: January 1, 2019 - December 31, 2020
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)

2018

  • Automatic Intraoperative Tracking for Workflow and Dose Monitoring in X-Ray-based Minimally Invasive Surgeries

    (Third Party Funds Single)

    Term: June 1, 2018 - May 31, 2021
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
    Abstract

    The goal of this project is the investigation of multimodal methods for the evaluation of interventional workflows in the operation room. This topic will be researched in an international project context with partners in Germany and in Brazil (UNISINOS in Porto Alegre). Methods will be developed to analyze the processes in an OR based on signals from body-worn sensors, cameras and other modalities like X-ray images recorded during the surgeries. For data analysis, techniques from the field of computer vision, machine learning and pattern recognition will be applied. The system will be integrated in a way that body-worn sensors developed by Portabiles as well as angiography systems produced by Siemens Healthcare can be included alongside.

2021

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2020

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Conference Contributions

2019

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Conference Contributions

2018

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2017

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2016

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Conference Contributions

2014

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