  This paper reports Deep LOGISMOS approach to 3D tumor segmentation by
incorporating boundary information derived from deep contextual learning to
LOGISMOS - layered optimal graph image segmentation of multiple objects and
surfaces. Accurate and reliable tumor segmentation is essential to tumor growth
analysis and treatment selection. A fully convolutional network (FCN), UNet, is
first trained using three adjacent 2D patches centered at the tumor, providing
contextual UNet segmentation and probability map for each 2D patch. The UNet
segmentation is then refined by Gaussian Mixture Model (GMM) and morphological
operations. The refined UNet segmentation is used to provide the initial shape
boundary to build a segmentation graph. The cost for each node of the graph is
determined by the UNet probability maps. Finally, a max-flow algorithm is
employed to find the globally optimal solution thus obtaining the final
segmentation. For evaluation, we applied the method to pancreatic tumor
segmentation on a dataset of 51 CT scans, among which 30 scans were used for
training and 21 for testing. With Deep LOGISMOS, DICE Similarity Coefficient
(DSC) and Relative Volume Difference (RVD) reached 83.2+-7.8% and 18.6+-17.4%
respectively, both are significantly improved (p<0.05) compared with contextual
UNet and/or LOGISMOS alone.
