Jinxiu Cai1, Xiaochao Guo1, Ke Wang1, Yaofeng Zhang2, Dadou Zhang2, Xiaodong Zhang1, Xiaoying Wang3. 1. Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China. 2. Beijing Smart Tree Medical Technology Co.Ltd, Beijing, China. 3. Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China. wangxiaoying@bjmu.edu.cn.
Abstract
OBJECTIVE: To develop a 3D U-Net-based model for the automatic segmentation of the pancreas using the diameters, volume, and density of normal pancreases among Chinese adults. METHODS: A total of 2778 pancreas images (dataset 1) were retrospectively collected and randomly divided into training (n = 2252), validation (n = 245), and test (n = 281) datasets. The segmentation model for the pancreas was constructed through cascaded application of two 3D U-Net networks. The segmentation efficiency for the pancreas was evaluated by the Dice similarity coefficient (DSC). Another dataset of 3189 normal pancreas CT images (dataset 2) was obtained for external validation, including 1063 non-contrast images, 1063 arterial phase images, and 1063 portal venous phase images. The pancreas segmentation in dataset 2 was assessed objectively and manually revised by two radiologists. Then, the pancreatic volume, diameters, and average CT value for each phase of pancreas images in dataset 2 were calculated. The relationships between pancreas volume and age, sex, height, and weight were analyzed. RESULTS: In dataset 1, a mean DSC of 0.94 for the test dataset was achieved. In dataset 2, the objective assessment yielded a 90% satisfaction rate for the automatic segmentation of the pancreas as external validation. The diameters of the pancreas were 43.71-44.28 mm, 67.40-68.15 mm, and 114.53-117.06 mm, respectively. The average pancreatic volume was 63,969.06-65,247.75 mm3, which was greatest at the age of 18-38 and then decreased to a minimum at the age of 69-85. The CT value of the pancreas also decreased with age, from a maximum value of 38.87 ± 9.70 HU to a minimum of 27.72 ± 10.85 HU. CONCLUSION: The pancreas segmentation tool based on deep learning can segment the pancreas on CT images and measure its normal diameter, volume, and CT value accurately and effectively.
OBJECTIVE: To develop a 3D U-Net-based model for the automatic segmentation of the pancreas using the diameters, volume, and density of normal pancreases among Chinese adults. METHODS: A total of 2778 pancreas images (dataset 1) were retrospectively collected and randomly divided into training (n = 2252), validation (n = 245), and test (n = 281) datasets. The segmentation model for the pancreas was constructed through cascaded application of two 3D U-Net networks. The segmentation efficiency for the pancreas was evaluated by the Dice similarity coefficient (DSC). Another dataset of 3189 normal pancreas CT images (dataset 2) was obtained for external validation, including 1063 non-contrast images, 1063 arterial phase images, and 1063 portal venous phase images. The pancreas segmentation in dataset 2 was assessed objectively and manually revised by two radiologists. Then, the pancreatic volume, diameters, and average CT value for each phase of pancreas images in dataset 2 were calculated. The relationships between pancreas volume and age, sex, height, and weight were analyzed. RESULTS: In dataset 1, a mean DSC of 0.94 for the test dataset was achieved. In dataset 2, the objective assessment yielded a 90% satisfaction rate for the automatic segmentation of the pancreas as external validation. The diameters of the pancreas were 43.71-44.28 mm, 67.40-68.15 mm, and 114.53-117.06 mm, respectively. The average pancreatic volume was 63,969.06-65,247.75 mm3, which was greatest at the age of 18-38 and then decreased to a minimum at the age of 69-85. The CT value of the pancreas also decreased with age, from a maximum value of 38.87 ± 9.70 HU to a minimum of 27.72 ± 10.85 HU. CONCLUSION: The pancreas segmentation tool based on deep learning can segment the pancreas on CT images and measure its normal diameter, volume, and CT value accurately and effectively.
Authors: Fee Klupp; Miriam Klauss; Nuh N Rahbari; Klaus Felix; Ulf Hinz; Ines Manglberger; Frank Bergmann; Matthias M Gaida; Thilo Hackert; Oliver Strobel; Markus W Büchler Journal: Surgery Date: 2019-10-18 Impact factor: 3.982