Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging. (December 2020)

• Record Type: Journal Article
• Authors: Qayyum, Abdul; Lalande, Alain; Meriaudeau, Fabrice
• Is Part Of: Computers in biology and medicine. Volume 127(2020)
• Publication Date: 2020-12
• Subjects: 3D volumetric segmentation -- 3D deep learning models -- 3D-residual network with SE -- Kidney and liver segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104097
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Proposed automatic 3D volumetric hybrid deep learning model for 3D volumetric segmentation. The 3D progressive Residual blocks with 3D Squeeze and Excitation (SE) module has been proposed for volumetric segmentation. The performance of proposed model has been validated using ablation study on abdominal CT datasets. The Kidney, kidney Tumor and Liver, Liver tumor CT 3D volume segmented by 3D proposed model. Proposed method produced excellent results in KiTS2019 dataset.