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Assignment Flow for Order-Constrained OCT Segmentation

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Pattern Recognition (DAGM GCPR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12544))

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Abstract

At the present time Optical Coherence Tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. Due to tissue-dependent speckle noise, the elaboration of automated segmentation models has become an important task in the field of medical image processing.

We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space. This makes it unbiased and therefore amenable for the detection of local anatomical changes of retinal tissue structure. To demonstrate robustness of the proposed approach we compare four different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in terms of mean absolute error and Dice similarity coefficient.

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Acknowledgement

We thank Dr. Stefan Schmidt and Julian Weichsel for sharing with us their expertise on OCT sensors, data acquisition and processing. In addition, we thank Prof. Fred Hamprecht and Alberto Bailoni for their guidance in training deep networks for feature extraction from 3D data.

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Authors and Affiliations

  1. Image and Pattern Analysis Group (IPA) and Heidelberg Collaboratory for Image Processing (HCI), Heidelberg University, Heidelberg, Germany

    Dmitrij Sitenko, Bastian Boll & Christoph Schnörr

Authors
  1. Dmitrij Sitenko
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  2. Bastian Boll
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  3. Christoph Schnörr
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Corresponding author

Correspondence to Dmitrij Sitenko .

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Editors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Zeynep Akata

  2. University of Tübingen, Tübingen, Germany

    Andreas Geiger

  3. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

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Sitenko, D., Boll, B., Schnörr, C. (2021). Assignment Flow for Order-Constrained OCT Segmentation. In: Akata, Z., Geiger, A., Sattler, T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science(), vol 12544. Springer, Cham. https://doi.org/10.1007/978-3-030-71278-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-71278-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71277-8

  • Online ISBN: 978-3-030-71278-5

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