SequentialSegNet: Combination with Sequential Feature for Multi-Organ Segmentation

Yao Zhang, Xuan Jiang, Cheng Zhong, Yang Zhang, Zhongchao Shi, Zhensheng Li, Zhiqiang He

Abstract: Multi-organ segmentation from computed tomography (CT) images is essential for computer aided diagnosis (CAD), and recent advances in fully convolutional networks (FCNs) for volumetric image segmentation have demonstrated the importance of leveraging spatial information. In this paper, we propose a novel framework called SequentialSegNet, which efficiently combines features within a single CT image (intra-slice) and among multiple adjacent images (inter-slice) for a multi-organ segmentation. Experimental results show that our approach can effectively improve the segmentation performance on both large-size and small-size abdominal organs including liver, spleen and gallbladder.