Advances on pancreas segmentation: a review

Abstract

Accurate pancreas segmentation from medical images is an important yet challenging problem for medical image analysis and medical surgery. Challenges relating to the pancreas image acquisition, the limited availability of image data and segmentation methodology hinder this segmentation task. This paper aims to present a systematic review of the different methodologies. Recently, a variety of segmentation methods have been proposed for automatic delineation of pancreas images. We intended to survey the methods proposed for image segmentation, outline those that have already been adopted for pancreas segmentation, perform a comparison between them using various indices and experimental data, and discuss their major contributions. Looking at the theoretical approach, different segmentation methods have been applied for delineating the pancreas and can be classified into those based on region, edge, atlas, neural network and other categories. Each segmentation method has its own advantages and disadvantages. Based on the previously performed analysis and discussion, and compared to other abdominal organs, improving the results of pancreas segmentation in the near future will require addressing several challenging issues. These issues include: (1) establishing publically available standardized datasets; (2) define clear metrics to evaluate the segmentation performance and (3) design new segmentation methods.