KTNet: Towards automated 2D kidney and tumor segmentation

Abstract

There are more than 400,000 new cases of kidney cancer each year, and surgery is its most common treatment. Accurate segmentation and characterization of kidneys and kidney tumors is an important step in quantifying the tumor's morphological details to monitor the progression of the disease and improve treatment planning. Segmentation of kidney tumors in CT images is a challenging task due to the low contrast, irregular motion, diverse shapes, and sizes. Furthermore, manual delineation techniques are extremely time-consuming and are prone to errors due to the variability between different specialists. The literature provides the application of 3D Convolutional Neural Networks (CNNs) for the segmentation of kidneys and tumors. While effective, 3D CNNs are computationally expensive. Our work proposes the applications of a novel 2D CNN architecture to segment kidneys and tumors from CT images. The proposed architecture uses features from enhanced images to improve the segmentation performance. Quantitative and qualitative analysis of the proposed model on the KiTS19 dataset shows the improvement against recent state-of-the-art architectures.