Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging

Automatic segmentation on computed tomography images of kidney and liver tumors remains a challenging task due to heterogeneity and variation in shapes. Recently, two-dimensional (2D) and three-dimensional (3D) deep convolutional neural networks have become popular in medical image segmentation tasks because they can leverage large labeled datasets, thus enabling them to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost of computational resources. In this paper, we propose a hybrid 3D residual network (RN) with a squeeze-and-excitation (SE) block for volumetric segmentation of kidney, liver, and their associated tumors. The proposed network uses SE blocks to capture spatial information based on the reweighting function in a 3D RN. This study is the first to use an SE residual mechanism to process medical volumetric images using the proposed 3D residual network composed of various combinations of residual blocks. Our framework was evaluated both on the Kidney Tumor Segmentation 2019 dataset and the public MICCAI 2017 Liver Tumor Segmentation dataset. The results show that our architecture outperforms other state-of-the-art methods. Moreover, the SE-RN achieves good performance in volumetric biomedical segmentation.