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J. Hladuvka, V. Renner:
"Towards Identification of Incorrectly Segmented OCT Scans";
Talk: Computer Vision and Robotics Workshop 2020, Österreich, TU Graz; 2020-04-16 - 2020-04-17; in: "Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020", (2020), ISBN: 978-3-85125-752-6; 7 pages.

Precise thickness measurements of retinallayers are crucial to decide whether the subject re-quires subsequent treatment. As optical coherencetomography (OCT) is becoming a standard imagingmethod in hospitals, the amount of retinal scans in-creases rapidly, automated segmentation algorithmsare getting deployed, and methods to assess theirperformance are in demand.In this work we propose a semi-supervised frame-work to detect incorrectly segmented OCT retinascans: ground-truth segmentations are (1) embed-ded in 2D feature space and (2) used to train an out-lier scoring function and the corresponding decisionboundary.We evaluate a selection of five outlier detectionmethods and find the results to be a promising start-ing point to address the given problem. While thiswork and results are centred around one concretesegmentation algorithm we sketch the possibilities ofhow the framework can be generalized for more re-cent or more precise segmentation methods.

http://dx.doi.org/10.3217/978-3-85125-752-6-36