Abstract
Automated segmentation of renal tumors is essential for the diagnostic evaluation of kidney cancer. However, renal tumor volume is generally small compared with the volume of the kidney and is irregularly distributed; moreover, the location and shape of renal tumors are highly variable, making the segmentation task extremely challenging. To solve the aforementioned problems, a cascaded segmentation model (FYU-Net) for computed tomography (CT) images is proposed in this paper to achieve automatic kidney tumor segmentation. The proposed model involves two main steps. In the first step, a fast scan of the kidney CT data is performed using a localization network to find slices containing tumors, and coarse segmentation is performed simultaneously. In the second step, a segmentation framework embedded with the feature pyramid network module is employed to finely segment kidney tumors. By building a feature pyramid structure, targets of different sizes are distributed to be detected on different feature layers to extract richer feature information. In addition, the top-down structure allows the information of the higher-level feature maps to be transferred to the lower-level feature maps, enhancing the semantic information of the lower-level feature maps. Comparative experiments were conducted on the Kidney PArsing Challenge 2022 public dataset; the average Jaccard coefficient and average Dice coefficient of tumor structure segmentation were more than 70.73% and more than 82.85%, respectively. The results demonstrate the effectiveness of the proposed model for kidney tumor segmentation.