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This paper reports Deep LOGISMOS approach to 3D tumor segmentation byincorporating boundary information derived from deep contextual learning toLOGISMOS - layered optimal graph image segmentation of multiple objects andsurfaces. Accurate and reliable tumor segmentation is essential to tumor growthanalysis and treatment selection. A fully convolutional network (FCN), UNet, isfirst trained using three adjacent 2D patches centered at the tumor, providingcontextual UNet segmentation and probability map for each 2D patch. The UNetsegmentation is then refined by Gaussian Mixture Model (GMM) and morphologicaloperations. The refined UNet segmentation is used to provide the initial shapeboundary to build a segmentation graph. The cost for each node of the graph isdetermined by the UNet probability maps. Finally, a max-flow algorithm isemployed to find the globally optimal solution thus obtaining the finalsegmentation. For evaluation, we applied the method to pancreatic tumorsegmentation on a dataset of 51 CT scans, among which 30 scans were used fortraining 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 contextualUNet and/or LOGISMOS alone.

Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans