Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution

Wolfgang Aichinger; Sebastian Krappe; A. Enis Cetin; Rengul Cetin-Atalay; Aysegül Üner; Michaela Benz; Thomas Wittenberg; Marc Stamminger; Christian Münzenmayer

doi:10.1117/12.2254036

1 March 2017 Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution

Wolfgang Aichinger, Sebastian Krappe, A. Enis Cetin, Rengul Cetin-Atalay, Aysegül Üner, Michaela Benz, Thomas Wittenberg, Marc Stamminger, Christian Münzenmayer

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400N (2017) https://doi.org/10.1117/12.2254036
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing.

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Wolfgang Aichinger, Sebastian Krappe, A. Enis Cetin, Rengul Cetin-Atalay, Aysegül Üner, Michaela Benz, Thomas Wittenberg, Marc Stamminger, and Christian Münzenmayer "Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400N (1 March 2017); https://doi.org/10.1117/12.2254036

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