Computerized medical imaging and graphics, 2015-12, Vol.46 (Pt 2), p.197-208
2015
Details
- Autor(en) / Beteiligte
- Titel
- Machine learning approaches to analyze histological images of tissues from radical prostatectomies
- Ist Teil von
- Computerized medical imaging and graphics, 2015-12, Vol.46 (Pt 2), p.197-208
- Ort / Verlag
- United States: Elsevier Ltd
- Erscheinungsjahr
- 2015
- Link zum Volltext
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- Highlights • Machine learning approaches were applied to separate stroma from epithelium in prostate tissue images. • Epithelium was sub-stratified into normal/benign and cancer areas. • Tissue content was predicted based on descriptors from individual pixels rather than from glands. • Tissue prediction does not involve detection of glandular lumens which is inaccurate, prone to errors, and has limitations. • Proposed method has the potential to aid in clinical prostate studies.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0895-6111eISSN: 1879-0771DOI: 10.1016/j.compmedimag.2015.08.002Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5062020
- Format
- –
- Schlagworte
- Algorithms, Epithelial Cells - pathology, Humans, Image analysis, Image Enhancement - methods, Image Interpretation, Computer-Assisted - methods, Internal Medicine, Machine Learning, Male, Microscopy - methods, Other, Pattern Recognition, Automated - methods, Prostate cancer, Prostatectomy - methods, Prostatic Neoplasms - pathology, Prostatic Neoplasms - surgery, Reproducibility of Results, Sensitivity and Specificity, Stromal Cells - pathology, Tissue classification, Tissue quantification, Treatment Outcome