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

: Aichinger, W.; Krappe, S.; Cetin, A.E.; Cetin-Atalay, R.; Üner, A.; Benz, M.; Wittenberg, T.; Stamminger, M.; Münzenmayer, C.


Gurcan, M.N. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2017. Digital Pathology : 12-13 February 2017, Orlando, Florida, United States
Bellingham, WA: SPIE, 2017 (Proceedings of SPIE 10140)
ISBN: 978-1-5106-0725-5
ISBN: 978-1-5106-0726-2
Paper 101400N, 6 S.
Conference "Medical Imaging - Digital Pathology" <5, 2017, Orlando/Fla.>
Fraunhofer IIS ()

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.