Yevgen Vengrenyuk

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Prostate tissue characteristics play an important role in predicting the recurrence of prostate cancer. Currently, experienced pathologists manually grade these prostate tissues using the Gleason scoring system, a subjective approach which summarizes the overall progression and aggressiveness of the cancer. Using advanced image processing techniques, Aureon(More)
We present a new system for automated localization and quantification of the expression of protein biomarkers in immunofluorescence (IF) microscopic images. The system includes a novel method for discriminating the biomarker signal from background, where signal may be the expression of any of the many biomarkers or counterstains used in IF. The method is(More)
Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of(More)
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