Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks

	Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks
	Wen Li; Fucang Jia; Qingmao Hu
刊名	Journal of Computer and Communications
	2015
英文摘要	Liver tumors segmentation from computed tomography (CT) images is an essential task for diagno-sis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algo-rithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross valida-tion. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06±1.63%, 82.67±1.43, and 84.34±1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.
收录类别	其他
原文出处	http://www.scirp.org/Journal/PaperInformation.aspx?PaperID=61314
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/7092]
专题	深圳先进技术研究院_医工所
作者单位	Journal of Computer and Communications
推荐引用方式 GB/T 7714	Wen Li,Fucang Jia,Qingmao Hu. Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks[J]. Journal of Computer and Communications,2015.
APA	Wen Li,Fucang Jia,&Qingmao Hu.(2015).Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks.Journal of Computer and Communications.
MLA	Wen Li,et al."Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks".Journal of Computer and Communications (2015).