Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

• DOI: 10.3390/diagnostics12081788
• Publication Date: 2022-07-25T02:49:02Z
• Authors: Dan Li; Chuda Xiao; Yang Liu; Zhuo Chen; Haseeb Hassan; Liyilei Su; Jun Liu; Haoyu Li; Weiguo Xie; Wen Zhong; Bingding Huang
• Keywords: Market Segmentation

Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) abdominal computed tomography (CT) images have been limited. Radiomics machine analyses diseases rely on the automatic kidneys CT images. Inspired by this, our primary aim is to utilize semantic models with a proposed training scheme achieve precise accurate outcomes. Moreover, this work aims provide community an open-source, unenhanced dataset testing networks segment detect kidney stones. Five variations are trained tested both dependently (based scheme) independently. Upon comparison, enable highly 2D 3D We believe fundamental step toward AI-driven diagnostic strategies, which can be essential component personalized patient care improved decision-making treating diseases.