Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging

ABSTRACT Objectives It is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. Our aim was to automatically determine the accurate DL using a machine learning method. Methods We used an unsupervised machine learning approach to determine the DLs because it can reduce the burden of training machine learning models and labeling large datasets. Our method consists of the following four steps: 1) An M-NBI image is segmented into superpixels (a group of neighboring pixels) using simple linear iterative clustering. 2) The image features are extracted for each superpixel. 3) The superpixels are grouped into several clusters using the k-means method. 4) The boundaries of the clusters are extracted as DL candidates. To validate the proposed method, 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted and was used to evaluate the proposed system. Results The average Euclidean distances for the 11 cases were10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the specific selection of the number of clusters enabled the proposed method to detect DLs that were similar to those of the endoscopists. The DLs identified by our method represented the complex shapes of the DLs, similarly to those identified by experienced doctors. Also, it was confirmed that the proposed system could generate the pathologically valid DLs by increasing the number of clusters. Conclusions Our proposed system can support the training of inexperienced doctors, as well as enrich the knowledge of experienced doctors in endoscopy.