title: Automated REcognition of tissue-associated erythrocytes (ARETE)-a new tool in tissue cytometry.
abstract: Automated microscopic image analysis of immunofluorescence-stained targets on tissue sections is challenged by autofluorescent elements such as erythrocytes, which might interfere with target segmentation and quantification. Therefore, we developed an automated system (Automated REcognition of Tissue-associated Erythrocytes; ARETE) for in silico exclusion of erythrocytes. To detect erythrocytes in transmission images, a cascade of boosted decision trees of Haar-like features was trained on 8,640/4,000 areas (15 × 15 pixels) with/without erythrocytes from images of placental sections (4 µm). Ground truth data were generated on 28 transmission images. At least two human experts labelled the area covered by erythrocytes. For validation, output masks of human experts and ARETE were compared pixel-wise against a mask obtained from majority voting of human experts. F1 score, specificity, and Cohen's κ coefficients were calculated. To study the influence of erythrocyte-derived autofluorescence, we investigated the expression levels of a protein (receptor for advanced glycated end products; RAGE) in placenta and number of Ki-67-positive/cytokeratin 8-positive epithelial cells in colon sections. ARETE exhibited high sensitivity (99.87%) and specificity (99.81%) on a training-subset and processed transmission images (1,392 × 1,024 pixels) within 4 sec. ARETE and human expert's F1-scores were 0.55 versus 0.76, specificities 0.85 versus 0.92 and Cohen's κ coefficients 0.41 versus 0.68. A ranking of Cohen's κ coefficient by the scale of Fleiss certified "good agreement" between ARETE and the human experts. Applying ARETE, we demonstrated 4-14% false-positive RAGE-expression in placenta, and 18% falsely detected proliferative epithelial cells in colon, caused by erythrocyte-autofluorescence. ARETE is a fast system for in silico reduction of erythrocytes, which improves automated image analysis in research and diagnostic pathology.
authors: Department of Pathophysiology and Allergy Research, Medical University Vienna, Vienna, Austria.
authors: Andreas
authors: A
authors: Heindl
authors: Alexander K
authors: AK
authors: Seewald
authors: Theresia
authors: T
authors: Thalhammer
authors: Giovanna
authors: G
authors: Bises
authors: Martin
authors: M
authors: Schepelmann
authors: Hana
authors: H
authors: Uhrova
authors: Sabine
authors: S
authors: Dekan
authors: Ildiko
authors: I
authors: Mesteri
authors: Radu
authors: R
authors: Rogojanu
authors: Isabella
authors: I
authors: Ellinger
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D003106:Colon / Q000648:ultrastructure*; D003663:Decision Trees; D004912:Erythrocytes / Q000737:chemistry / Q000166:cytology*; D005260:Female; D005453:Fluorescence; D015870:Gene Expression; D006801:Humans; D019044:Image Cytometry / Q000295:instrumentation / Q000379:methods*; D053533:Keratin-8 / Q000235:genetics / Q000378:metabolism; D019394:Ki-67 Antigen / Q000235:genetics / Q000378:metabolism; D010920:Placenta / Q000648:ultrastructure*; D011247:Pregnancy; D000067759:Receptor for Advanced Glycation End Products / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D015415:Biomarkers; D053533:Keratin-8; D019394:Ki-67 Antigen; D000067759:Receptor for Advanced Glycation End Products