Identification of optimal semantic segmentation architecture for the segmentation of hepatic structures from computed tomography images

Automatic segmentation of various hepatic structures from computed tomography images is of great importance in surgery and treatment planning for patients diagnosed with liver cancer. This work tends to identify the optimal semantic segmentation architecture for the task of segmentation of hepatic structures from computed tomography (CT) images. Segmentation of hepatic structures from CT images is carried out using four popular semantic segmentation architectures viz. fully convolutional network (FCN), SegNet, U-Net and DeepLabV3 + with various encoder configurations on 3D-ircadb-01, CHAOS and on an institutional dataset images. Segmentation of liver, liver tumour, hepatic artery, portal vein and hepatic vein are carried out simultaneously using a single network as a dense pixel prediction task. An extensive experimental assessment of hepatic structures segmentation was carried out using 13 networks derived from the aforesaid 4 segmentation architectures. The segmentation performance is assessed in terms of segmentation accuracy, dice coefficient, Jaccard index and boundary F1score. Experimental results show that DeepLabV3 + architecture with Xception encoder records better segmentation performance with dice coefficient of 98.13% 98.87% and 98.92% on 3D-ircadb-01, CHAOS and on the institutional datasets respectively. The comparison with the state-of-the-art techniques presented exemplifies the effectiveness of the fully depth separable convolutional network on the segmentation of various hepatic structures. Depth separable convolution applied to atrous spatial pyramid pooling (ASPP) with multiple sampling rates at the decoder of the DeepLabV3 + architecture together with the entirely separable convolution based Xception model as encoder stands as evidence towards the exceptional segmentation performance of hepatic structures.