MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images

Automatic liver and tumor segmentation play a significant role in clinical interpretation and treatment planning of hepatic diseases. To segment liver and tumor manually from the hundreds of computed tomography (CT) images is tedious and labor-intensive; thus, segmentation becomes expert dependent. In this paper, we proposed the multi-scale approach to improve the receptive field of Convolutional Neural Network (CNN) by representing multi-scale features that extract global and local features at a more granular level. We also recalibrate channel-wise responses of the aggregated multi-scale features that enhance the high-level feature description ability of the network. The experimental results demonstrated the efficacy of a proposed model on a publicly available 3Dircadb dataset. The proposed approach achieved a dice similarity score of 97.13 % for liver and 84.15 % for tumor. The statistical significance analysis by a statistical test with a p-value demonstrated that the proposed model is statistically significant for a significance level of 0.05 (p-value < 0.05). The multi-scale approach improves the segmentation performance of the network and reduces the computational complexity and network parameters. The experimental results show that the performance of the proposed method outperforms compared with state-of-the-art methods.