Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

Wilm F, Benz M, Bruns V, Baghdadlian S, Dexl J, Hartmann D, Kuritcyn P, Weidenfeller M, Wittenberg T, Merkel S, Hartmann A, Eckstein M, Geppert CI (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Journal of Medical Imaging Society of Photo-optical Instrumentation Engineers (SPIE)

Book Volume: 9

Journal Issue: 2

DOI: 10.1117/1.JMI.9.2.027501

Abstract

Purpose: Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSIs), however, poses a challenge in terms of computation time. In this regard, the analysis of nonoverlapping patches outperforms pixelwise segmentation approaches but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies.

Authors with CRIS profile

Frauke Wilm Lehrstuhl für Informatik 5 (Mustererkennung) Thomas Wittenberg Chair of Visual Computing (LGDV) Susanne Merkel Department of Surgery Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Markus Eckstein Institute of Pathology Carol-Immanuel Geppert Institute of Pathology

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Wilm, F., Benz, M., Bruns, V., Baghdadlian, S., Dexl, J., Hartmann, D.,... Geppert, C.-I. (2022). Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification. Journal of Medical Imaging, 9(2). https://doi.org/10.1117/1.JMI.9.2.027501

MLA:

Wilm, Frauke, et al. "Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification." Journal of Medical Imaging 9.2 (2022).

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