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Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study
J J W Tischendorf1, S Gross, R Winograd
1Medical Department III (Gastroenterology, Hepatology and Metabolic Diseases), Aachen University Hospital, RWTH Aachen University, 52074 Aachen, Germany. jtischendorf@ukaachen.de
Background And Study Aims:
Recent studies have shown that narrow-band imaging (NBI) is a powerful diagnostic tool for differentiating between neoplastic and nonneoplastic colorectal polyps. The aim of the present study was to develop and evaluate a computer-based method for automated classification of colorectal polyps on the basis of vascularization features.
Patients And Methods:
In a prospective pilot study with 128 patients who were undergoing zoom NBI colonoscopy, 209 detected polyps were visualized and subsequently removed for histological analysis. The proposed computer-based method consists of image preprocessing, vessel segmentation, feature extraction, and classification. The results of the automated classification were compared to those of human observers blinded to the histological gold standard.
Results:
Consensus decision between the human observers resulted in a sensitivity of 93.8 % and a specificity of 85.7 %. A "safe" decision, i. e., classifying polyps as neoplastic in cases when there was interobserver discrepancy, yielded a sensitivity of 96.9 % and a specificity of 71.4 %. The overall correct classification rates were 91.9 % for the consensus decision and 90.9 % for the safe decision. With ideal settings the computer-based approach achieved a sensitivity of approximately 90 % and a specificity of approximately 70 %, while the overall correct classification rate was 85.3 %. The computer-based classification showed a specificity of 61.2 % when a sensitivity of 93.8 % was selected, and a 53.1 % specificity with a sensitivity of 96.9 %.
Conclusions:
Automated classification of colonic polyps on the basis of NBI vascularization features is feasible, but classification by observers is still superior. Further research is needed to clarify whether the performance of the automated classification system can be improved.
