A Novel Deep Learning Model for Pancreas Segmentation: Pascal U-Net

Abstract

A robust and reliable automated organ segmentation from abdomen images is a crucial problem in both quantitative imaging analysis and computer aided diagnosis. Especially, automatic pancreas segmentation from abdomen CT images is most challenging task which based on in two main aspects (1) high variability in anatomy (like as shape, size, etc.) and location across different patients (2) low contrast with neighboring tissues. Due to these reasons, achievement of high accuracies in pancreas segmentation is hard image segmentation problem. In this paper, we propose a novel deep learning model which is convolutional neural network-based model called Pascal U-Net for pancreas segmentation. Performance of the proposed model is evaluated on The Cancer Imaging Archive (TCIA) Pancreas CT database and abdomen CT dataset which is taken from Selcuk University Medicine Faculty Radiology Department. During the experimental studies, k-fold cross-validation method is used. Furthermore, results of the proposed model are compared with results of traditional U-Net. If results obtained by Pascal U-Net and traditional U-net for different batch size and fold number is compared, it can be seen that experiments on both datasets validate the effectiveness of Pascal U-Net model for pancreas segmentation.