Posts Tagged ‘3D Cascaded U-Net’
Complete List of Challenge Submissions
Fully Automatic Segmentation of Kidney and Tumor Based on Cascaded U-Nets
Li, Yu
Fully automatic segmentation of kidney and its lesions is an important step to obtain accurate clinical diagnosis and computer aided decision support system. In this paper, a method of automatic segmentation of kidney and renal tumor in CT abdominal images using cascade 3D U-Net convolutional neural network (3D cU-Nets) is presented. We trained and cascaded two 3D U-Nets for the joint segmentation of kidney and renal tumor. In the first step, we trained a 3D U-Net to segment kidney as the ROI input for the second 3D u-net.The second 3D U-Net only segmented the lesion from the renal ROI predicted in step 1.
3D Cascaded U-Net with a Squeeze-and-Exicitation Block for Semantic Segmentation on Kidney and Renal Cell Carcinoma in Abdonimal Volumetric CT
Ham, Sungwon
Kim, Sungchul
Yun, Jihye
Chang, Yongjin
Yu, Donghoon
Seo, Hyungi
Yoon, MyungKi
Soo Kyung, Yoon
Kim, Namkug
Segmentation is a fundamental process in medical image analysis. Recently, convolutional neural networks (CNNs) has allowed for automatic segmentation; however, segmentaiton of complex organs and diseases including the kidney or renal cell carcinoma (RCC) remains a different task due to limited data and labor-intensive labeling work. The purpose of this study is to segment kideny and RCC in CT using cascaded 3D U-Net with a squeeze-and-excitation (SE) block using a cascaded method. 210 kidneys and their RCC in abdominal CT images were used as training and validation sets. The Dice similarity coefficients (DSCs) of kidney and RCC in test set were 0.963 and 0.734 respectively. The cascaded semantic segmentation can potentially reduce segmentation efforts and increase the efficiency in clinical workflow.