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Unsupervised Segmentation of Wounds in Optical Coherence Tomography Images Using Invariant Information Clustering

Julia Andresen; Timo Kepp; Michael Wang-Evers; Jan Ehrhardt; Dieter Manstein; Heinz Handels
In: Klaus Maier-Hein; Thomas M Deserno; Heinz Handels; Andreas Maier; Christoph Palm; Thomas Tolxsdorff (Hrsg.). Bildverarbeitung für die Medizin 2022. Pages 1-6, Informatik aktuell (INFORMAT), Springer, 2022.


Monitoring wound healing with optical coherence tomography (OCT) imaging is a promising research field. So far, however, few data and even less manual annotations of OCT wound images are available. To address this problem, a fully unsupervised clustering method based on convolutional neural networks (CNNs) is presented. The CNN takes image patches as input and assigns them to either wound or healthy skin clusters. Network training is based on a new combination of loss functions that require information invariance and locality preservation. No expensive expert annotations are needed. Locality preservation is applied to different levels of the network and shown to improve the segmentation. Promising results are achieved with an average Dice score of 0.809 and an average rand index of 0.871 for the best performing network version.

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