CT Guided Diagnosis: Cascaded U-Net for 3D Segmentation of Liver and Tumor

Abstract

Abstract Volumetric estimation of the liver tumor is the first step to identifying the livers critical disorder. The liver and its tumor ratio prerequisite measures to select the therapeutic procedure. 3D printing and virtual reality platform require a segmented liver entity mask to evaluate the pre and post-treatment analysis. A cascaded U-Net model is proposed for automatic segmentation of liver and tumor in CT images. LiTS CT data set utilized for this study. The images were pre-processed using the windowing technique for contrast enhancement. Two U-Net models were modified for liver and tumor segmentation, respectively and connected in a cascaded manner. U-Net decoder end was modified in comparison to the original U-Net. The probability map of the first U-Net fed to the second U-Net and the input image to segment out the liver tumor. Eight subject volumetric CT datasets were utilized to test the cascaded U-Net performance and achieved average Dice coefficient for liver and tumor 0.95 and 0.69, respectively. Liver tumor diagnosis and treatment accuracy depend upon the precision of segmentation algorithms. Designed model segmented liver almost accurately and tumor segmented with limited accuracy. A further modification is required for the tumor segmentation cause of the occurrence of false negative.