Zusammenfassung
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Literatur
Ji X, Henriques JF, Vedaldi A. Invariant information clustering for unsupervised image classification and segmentation. Proc IEEE ICCV. 2019:9865–74.
Do K, Tran T, Venkatesh S. Clustering by maximizing mutual information across views. Proc IEEE ICCV. 2021:9928–38.
Aljalbout E, Golkov V, Siddiqui Y, Strobel M, Cremers D. Clustering with deep learning: taxonomy and new methods. arXiv preprint. 2018;arXiv:1801.07648.
Huang P, Huang Y, Wang W, Wang L. Deep embedding network for clustering. Proc IEEE ICRR. 2014:1532–7.
Van der Maaten L, Hinton G.Visualizing data using t-SNE. J Mach LearnRes. 2008;9(11):2579– 605.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Andresen, J., Kepp, T., Wang-Evers, M., Ehrhardt, J., Manstein, D., Handels, H. (2022). Unsupervised Segmentation of Wounds in Optical Coherence Tomography Images Using Invariant Information Clustering. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-658-36932-3_1
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-36931-6
Online ISBN: 978-3-658-36932-3
eBook Packages: Computer Science and Engineering (German Language)