Automated Quantification of Cellular Structures in Histological Images
Veröffentlichungsdatum
2019-09-02
Autoren
Betreuer
Gutachter
Zusammenfassung
Examination of tissue in pathology plays a central role in many diseases, including most cancers. Pathologists are remarkably good at conducting qualitative investigations, including finding and understanding different tissue patterns and textures. However, quantitative examinations, which are mostly required for the assessment of cellular structures, contain large inter- and intra-observer variability. Automated quantification of cellular structures using digitized histological tissue sections promises to improve accuracy, reproducibility and efficiency of quantitative assessments. However, histological images exhibit large variability, artifacts and clustered structures, which presents a challenge for automated analysis. This cumulative dissertation aims at bringing the automated quantification of cellular structures closer to practical applicability. To this end, efficient analyses will be developed that are optimized with regard to these challenges.
Schlagwörter
Digital Pathology
;
Histology
;
Image Analysis
;
Deep learning
;
Machine Learning
;
Nuclei Detection
;
Biomarker Quantification
Institution
Fachbereich
Dokumenttyp
Dissertation
Zweitveröffentlichung
Nein
Sprache
Englisch
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00108607-1.pdf
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16.1 MB
Format
Adobe PDF
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