Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans

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


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.



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.


  1. 1.
    Chiu, S.J., Izatt, J.A., O’Connell, R.V., Winter, K.P., Toth, C.A., Farsiu, S.: Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest. Ophthalmol. Vis. Sci. 53(1), 53 (2012)CrossRefGoogle Scholar
  2. 2.
    Chiu, S.J., Allingham, M.J., Mettu, P.S., Cousins, S.W., Izatt, J.A., Farsiu, S.: Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express 6(4), 1172–1194 (2015)CrossRefGoogle Scholar
  3. 3.
    Karri, S., Chakraborthi, D., Chatterjee, J.: Learning layer-specific edges for segmenting retinal layers with large deformations. Biomed. Opt. Express 7(7), 2888–2901 (2016)CrossRefGoogle Scholar
  4. 4.
    Rathke, F., Schmidt, S., Schnörr, C.: Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization. Med. Image Anal. 18(5), 781–794 (2014)CrossRefGoogle Scholar
  5. 5.
    Poon, H., Domingos, P.: Sum-product networks: A new deep architecture. In: UAI, pp. 337–346 (2011)Google Scholar
  6. 6.
    Tian, J., Varga, B., Tatrai, E., Fanni, P., Somfai, G.M., Smiddy, W.E., Debuc, D.C.: Performance evaluation of automated segmentation software on optical coherence tomography volume data. J. Biophotonics 9(5), 478–489 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabian Rathke
    • 2
    Email author
  • 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

Personalised recommendations