MCMC Guided CNN Training and Segmentation for Pancreas Extraction

Abstract

Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What’s more, the pancreas only occupies a small portion in abdomen, and the organ border is very fuzzy. All these factors make the segmentation methods of other organs less suitable for pancreas. In this work, we propose a Markov Chain Monte Carlo (MCMC) guided convolutional neural network (CNN) approach, in order to handle such difficulties in morphological and photometric variabilities. Specifically, the proposed method mainly consists of three steps: First, registration is carried out to mitigate the body weight and location variability. Then, an MCMC scheme is designed to guide the adaptive selection of 3D patches, which are fed to the CNN for training. At the same time, the pancreas distribution is also learned for subsequent segmentation. Eventually, the same MCMC process guides the segmentation process with patch-wise predictions fused using a Bayesian voting scheme. This method is evaluated on the NIH pancreatic dataset including 82 abdominal contrast-enhanced CT volumes. We have achieved a competitive result of 78.13% Dice Similarity Coefficient value and 82.65% Recall value in testing data.

Keywords

• Pancreas segmentation
• image registration
• MCMC
• 3D convolutional neural network