MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network

Abstract

The segmentation of the kidney tumor and the quantification of its tumor indices (i.e., the center point coordinates, diameter, circumference, and cross-sectional area of the tumor) are important steps in tumor therapy. These quantifies the tumor morphometrical details to monitor disease progression and accurately compare decisions regarding the kidney tumor treatment. However, manual segmentation and quantification is a challenging and time-consuming process in practice and exhibit a high degree of variability within and between operators. In this paper, MB-FSGAN (multi-branch feature sharing generative adversarial network) is proposed for simultaneous segmentation and quantification of kidney tumor on CT. MB-FSGAN consists of multi-scale feature extractor (MSFE), locator of the area of interest (LROI), and feature sharing generative adversarial network (FSGAN). MSFE makes strong semantic information on different scale feature maps, which is particularly effective in detecting small tumor targets. The LROI extracts the region of interest of the tumor, greatly reducing the time complexity of the network. FSGAN correctly segments and quantifies kidney tumors through joint learning and adversarial learning, which effectively exploited the commonalities and differences between the two related tasks. Experiments are performed on CT of 113 kidney tumor patients. For segmentation, MB-FSGAN achieves a pixel accuracy of 95.7%. For the quantification of five tumor indices, the R² coefficient of tumor circumference is 0.9465. The results show that the network has reliable performance and shows its effectiveness and potential as a clinical tool.

Keywords: Feature commensal; Generative adversarial; Multi-scale; Quantification; Segmentation.