Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution - Aichinger, Wolfgang; Krappe, Sebastian; Enis, A. Cetin; Cetin-Atalay, Rengul; Üner, Aysegül; Benz, Michaela; Wittenberg, Thomas; Stamminger, Marc; Münzenmayer, Christian (Proceedings Volume 10140 Medical Imaging 2017:Digital Pathology, 2017)
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.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: A comparison of two classification schemes with respect to the segmentation quality - Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian (Proc. SPIE 9414,, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD9.automa
Keywords: Hematology; classification; segmentation; microscopy

Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks - Krappe, Sebastian; Benz, Michaela; Gryanik, Alexander; Tannich, Egbert; Wegner, Christine; Stamminger, Marc; Wittenberg, Thomas; Münzenmayer, Christian (Medical Imaging: Digital Pathology 2017, 2017)
Malaria is one of the world's most common and serious tropical diseases, caused by parasites of the genus plasmodia that are transmitted by Anopheles mosquitoes. Various parts of Asia and Latin America are affected but highest malaria incidence is found in Sub-Saharan Africa. Standard diagnosis of malaria comprises microscopic detection of parasites in stained thick and thin blood films. As the process of slide reading under the microscope is an error-prone and tedious issue we are developing computer-assisted microscopy systems to support detection and diagnosis of malaria.In this paper we focus on a deep learning (DL) approach for the detection of plasmodia and the evaluation of the proposed approach in comparison with two reference approaches. The proposed classification schemes have been evaluated with more than 180,000 automatically detected and manually classified plasmodia candidate objects from socalled thick smears. Automated solutions for the morphological analysis of malaria blood films could apply such a classifier to detect plasmodia in the highly complex image data of thick smears and thereby shortening the examination time. With such a system diagnosis of malaria infections should become a less tedious, more reliable and reproducible and thus a more objective process. Better quality assurance, improved documentation and global data availability are additional benefits.
Keywords: Malaria diagnosis; automated microscopy; image analysis; machine learning; plasmodia recognition; convolutional neural networks

Dynamic programming for the segmentation of bone marrow cells - Krappe, Sebastian; Münzenmayer, Christian; Evert, Amrei; Can, Koyuncu; Cetin, Enis; Haferlach, Torsten; Wittenberg, Thomas; Held, Christian (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD9.dynami_5
Keywords: Hematology; classification; segmentation; microscopy