Classification of pancreatic tumors using colored two-dimensional histograms from ultrasound endoscopic images

Abstract

The diagnosis using a time-intensity curve (TIC) is considered to be useful in the differentiation of pancreatic tumors. TIC is a graph that shows a contrast intensity of contrast-enhanced endoscopic ultrasonography over time. We propose a method to classify pancreatic tumors, which generates and uses two types of images representing a contrast effect from ultrasound endoscopic images. The first type is a two-dimensional histogram that adds information about a distribution of luminance values per frame to TIC which features a contrast effect over time. The second type is a frame with the highest average luminance value among all frames of each case. The frame featured a contrast enhancement pattern of the tumor. The features of the two images were extracted using deep learning. The two extracted features were combined by a concatenate layer. The combined feature outputs by a fully connected layer as the probability of pancreatic cancer. In this study, 131 cases with pancreatic tumors (pancreatic cancer: 86 cases, non-pancreatic cancer: 45 cases) were used. As a result of receiver operating characteristic analysis of the output probability, the area under the curve was 0.82, the sensitivity was 80.2%, and the specificity was 71.1%.