Improving Generalization Capability of Multi-Organ Segmentation Models Using Dual-Energy CT

Abstract

Multi-organ segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft tissue contrast, noise, and artifacts. Devising automatic multi-organ segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multi-center/scanner analyses. In this study, we investigate the use of dual-energy CT images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e. at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using dual-energy CT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with p≤0.03. Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available dual-energy CT data.