In order to realize fast and accurate segmentation of organs in Computed Tomography (CT) images, a automatic segmentation algorithm for single organ based on cascaded Vnet-S network was proposed. Firstly, the organ in the CT image was coarsely segmented by using the first Vnet-S network. Then, the maximum connection flux in the segmentation result was selected and expanded twice, and the organ boundary was determined and the organ area was extracted according to the maximum connection flux after expansion. Finally, the organ was finely segmented by using the second Vnet-S network. In order to verify the performance of the proposed algorithm, a liver segmentation experiment was carried out on the MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) dataset, and a lung segmentation experiment was carried out on the ISBI LUng Nodule Analysis 2016 (LUNA16) dataset. The cascaded Vnet-S algorithm has a Dice coefficient of 0.9600 on the online test data of 70 cases in LiTS and a Dice coefficient of 0.9810 on the 288 cases in LUNA16, which are higher than those of Vnet-S network and Vnet network. Experimental results show that the single organ segmentation algorithm based on cascaded Vnet-S network can accurately segment organs with lower computational complexity compared with Vnet and Unet networks.