A systematic review on automatic segmentation of renal tumors and cysts using various convolutional neural network architectures in radiological images.

Abstract

Premature diagnosis of kidney cancer is crucial for saving lives and enabling better treatment. Medical experts utilize radiological images, such as CT, MRI, US, and histopathological analysis, to identify kidney tumors and cysts, providing valuable information on their size, shape, location, and metabolism, thus aiding in diagnosis. In radiological image processing, precise segmentation remains difficult when done manually, despite numerous noteworthy efforts and encouraging results in this field. Thus, there's an emergent need for automatic methods for renal and renal mass segmentation. In this regard, this article reviews studies on utilizing deep learning models to detect renal masses early in medical imaging examinations, particularly various CNN (Convolutional Neural Network) models that have demonstrated excellent outcomes in the segmentation of radiological images. Furthermore, we addressed the detailed dataset characteristics that the researchers adapted, as well as the accuracy and efficiency metrics obtained using various parameters. However, several studies employed datasets with limited images, whereas only a handful used hundreds of thousands of images. Those examinations did not fully determine the tumor and cyst diagnosis. The key goals are to describe recent accomplishments, examine the methodological approaches used by researchers, and recommend potential future research directions.