Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/9944
Title: Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma
Authors: Σπυρίδωνος, Παναγιώτα Π.
Κάβουρας, Διονύσης Α.
Ραβαζούλα, Παναγιώτα
Νικηφορίδης, Γεώργιος Χ.
Item type: Journal article
Keywords: Histologic bladder sections;Neural computers;Ιστολογικές τομές κύστης;Νευρωνικό δίκτυο
Subjects: Medicine
Biomedical engineering
Ιατρική
Βιοϊατρική τεχνολογία
Issue Date: 8-May-2015
2002
Publisher: Journal of Reproductive Medicine
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.
Language: English
Citation: 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.
Journal: Analytical and Quantitative Cytology and Histology
Type of Journal: With a review process (peer review)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/9944
Appears in Collections:Δημοσιεύσεις

Files in This Item:
There are no files associated with this item.


This item is licensed under a Creative Commons License Creative Commons