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Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans

  • Fabian Rathke
  • Mattia Desana
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Segmenting retinal tissue deformed by pathologies can be challenging. Segmentation approaches are often constructed with a certain pathology in mind and may require a large set of labeled pathological scans, and therefore are tailored to that particular pathology.

We present an approach that can be easily transfered to new pathologies, as it is designed with no particular pathology in mind and requires no pathological ground truth. The approach is based on a graphical model trained for healthy scans, which is modified locally by adding pathology-specific shape modifications. We use the framework of sum-product networks (SPN) to find the best combination of modified and unmodified local models that globally yield the best segmentation. The approach further allows to localize and quantify the pathology. We demonstrate the flexibility and the robustness of our approach, by presenting results for three different pathologies: diabetic macular edema (DME), age-related macular degeneration (AMD) and non-proliferative diabetic retinopathy.

Notes

Acknowledgments

This work has been supported by the German Research Foundation (DFG) within the programme “Spatio-/Temporal Graphical Models and Applications in Image Analysis”, grant GRK 1653.

References

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabian Rathke
    • 2
  • Mattia Desana
    • 1
  • Christoph Schnörr
    • 1
    • 2
  1. 1.Image and Pattern Analysis Group (IPA)University of HeidelbergHeidelbergGermany
  2. 2.Heidelberg Collaboratory for Image Processing (HCI)HeidelbergGermany

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