Authors
Ham, Sungwon
Kim, Sungchul
Yun, Jihye
Chang, Yongjin
Yu, Donghoon
Seo, Hyungi
Yoon, MyungKi
Soo Kyung, Yoon
Kim, Namkug
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
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.
Identifiers
doi: 10.24926/548719.033