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

Aichinger, Wolfgang
Krappe, Sebastian
Atalay, Rengül
Benz, Michaela
Wittenberg, Thomas
Stamminger, Marc
Muenzenmayer, Christian
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 & Eosin (H&E) staining as an inexpensive tool for routine histopathology based on their distinct morphological features.


Mercadier, Deniz Sayin; Beşbınar, Beril; Frossard, Pascal (2019-01-01)
Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming traditional approaches that exploit color and texture features in combination with shallow classifiers o...
Convolutional Neural Networks for Classification of Colorectal Cancer on Whole Slide Images
Temena, Mehmet Arda; Isildak, Ulas; Acar, Ahmet (2021-03-27)
Diagnosis of cancer is typically performed by medical pathologist visually inspecting hematoxylin and eosin (H&E) stained whole slide images. Although the early detection of various cancer types is crucial for a successful treatment, it is a challenging work due to inter- and intra-personal variability and it may often result in a disagreement between pathologists. Visual inspection of medical specimen is a well- established method for cancer diagnosis, but digital transformation is now inevitable fact, whe...
Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods
Işın, Ali; Direkoğlu, Cem; Şah, Melike (Elsevier BV; 2016)
Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain ...
CAD for detection of microcalcification and classification in mammograms
AKBAY, Cansu; Gençer, Nevzat Güneri; GENÇER, Gülay (2014-10-17)
In this study, computer aided diagnosis (CAD) is developed to detect microcalficication cluster which is one of the important radiological findings of breast cancer diagnosis and classificiation. For this purpose, image processing and pattern recognition algorithms are applied on mamographic images. To make microcalcifications more visible wavelet transform and nonsubsampled contourlet transform (NSCT) methods are used for image enhancement. Their performances are compared. 52 features are extracted from th...
Examination of the dielectrophoretic spectra of MCF7 breast cancer cells and leukocytes
Çağlayan, Zeynep; Demircan Yalçın, Yağmur; Külah, Haluk (Wiley, 2020-03-01)
The detection of circulating tumor cells (CTCs) in blood is crucial to assess metastatic progression and to guide therapy. Dielectrophoresis (DEP) is a powerful cell surface marker-free method that allows intrinsic dielectric properties of suspended cells to be exploited for CTC enrichment/isolation from blood. Design of a successful DEP-based CTC enrichment/isolation system requires that the DEP response of the targeted particles should accurately be known. This paper presents a DEP spectrum method to inve...
Citation Formats
W. Aichinger et al., “Automated cancer stem cell recognition in H&E stained tissue using convolutional neural networks and color deconvolution,” 2017, vol. 10140, p. 0, Accessed: 00, 2020. [Online]. Available: