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. In particular, automatic pancreas segmentation from abdomen CT images is the most challenging task based on two main aspects (1) high variability in anatomy (like as shape, size, etc.) and location across different patients and (2) low contrast with neighbouring tissues. Due to these reasons, the achievement of high accuracies in pancreas segmentation is a hard image segmentation problem. In this paper, we propose a novel deep learning model which is a convolutional neural network-based model called Pascal U-Net for pancreas segmentation. The 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, the k-fold cross-validation method is used. Furthermore, the results of the proposed model are compared with the results of traditional U-Net. If results obtained by Pascal U-Net and traditional U-Net for different batch sizes and fold number is compared, it can be seen that experiments on both datasets validate the effectiveness of the Pascal U-Net model for pancreas segmentation.