Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma

dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-05-08T10:03:39Z
dc.date.issued	2015-05-08
dc.identifier.uri	http://hdl.handle.net/11400/9944
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://europepmc.org/abstract/med/12508689	en
dc.subject	Histologic bladder sections
dc.subject	Neural computers
dc.subject	Ιστολογικές τομές κύστης
dc.subject	Νευρωνικό δίκτυο
dc.title	Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh87008041
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2002
heal.bibliographicCitation	Spyridonos, P., Cavouras, D., Ravazoula, P. and Nikiforidis, G. (2002). Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma. Analytical and Quantitative Cytology and Histology. 24(60. pp. 317-324. Journal of Reproductive Medicine: 2002.	en
heal.abstract	OBJECTIVE: To develop an image analysis system for automated nuclear segmentation and classification of histologic bladder sections employing quantitative nuclear features. STUDY DESIGN: Ninety-two cases were classified into three classes by experienced pathologists according to the WHO grading system: 18 cases as grade 1, 45 as grade 2, and 29 as grade 3. Nuclear segmentation was performed by means of an artificial neural network (ANN)-based pixel classification algorithm, and each case was represented by 36 nuclei features. Automated grading of bladder tumor histologic sections was performed by an ANN classifier implemented in a two-stage hierarchic tree. RESULTS: On average, 95% of the nuclei were correctly detected. At the first stage of the hierarchic tree, classifier performance in discriminating between cases of grade 1 and 2 and cases of grade 3 was 89%. At the second stage, 79% of grade 1 cases were correctly distinguished from grade 2 cases. CONCLUSION: The proposed image analysis system provides the means to reduce subjectivity in grading bladder tumors and may contribute to more accurate diagnosis and prognosis since it relies on nuclear features, the value of which has been confirmed.	en
heal.publisher	Journal of Reproductive Medicine	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false