Comparison of deep learning-based techniques for organ segmentation in abdominal CT images

V. Groza, T. Brosch, D. Eschweiler, H. Schulz, S. Renisch, H. Nickisch

Abstract: Automatic segmentation of the liver, spleen and both kidneys is an important problem allowing to achieve accurate clinical diagnosis and to improve computer- aided decision support systems. This work presents a computational methods for automatic segmentation of liver, spleen, left and right kidney in abdominal CT images using deep convolutional neural networks (CNN) which allow the accurate segmentation of large-scale medical trials. Moreover this work demonstrates the comparison of several CNN based approaches to perform the segmentation of required organs. Validation results on the given dataset show that U-Net based liver, spleen and both kidneys segmentation for transaxial slicing achieves mean Dice similarity scores (DSC) of 94%, 89% and 88% respectively.

Author Affiliation: Philips Innovation Labs

Keywords: deep learning, medical imaging, image processing, neural networks