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Neural network-based segmentation and classification system for automated grading of histologic sections of bladder
Panagiota Spyridonos1, Dionisios Cavouras, Panagiota Ravazoula
1Computer Laboratory, School of Medicine, University of Patras, 265 00 Rio-Patras, Greece.
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.