A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images. Issue 1 (6th December 2021)

• Record Type: Journal Article
• Authors: Gong, Zhaoxuan; Guo, Cui; Guo, Wei; Zhao, Dazhe; Tan, Wenjun; Zhou, Wei; Zhang, Guodong
• Is Part Of: Journal of applied clinical medical physics. Volume 23:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-06
• Subjects: active contour model -- convolutional neural networks -- CT image -- fractional differential -- liver segmentation; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.13482
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.