Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound

被引：110

作者：

Wu, Kaizhi ^{[1
,3
]}

Chen, Xi ^{[2
]}

Ding, Mingyue ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Life Sci & Technol, Dept Biomed Engn, Key Lab Image Proc & Intelligent Control,Educ Min, Wuhan 430074, Peoples R China

[2] Huazhong Univ Sci & Technol, Inst Pattern Recognit & Artificial Intelligence, Sci & Technol Multispectral Informat Proc Lab, Wuhan 430074, Peoples R China

[3] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330063, Peoples R China

来源：

OPTIK | 2014年 / 125卷 / 15期

关键词：

Deep learning; Contrast-enhanced ultrasound; Focal liver lesions; FACE DETECTION; DIAGNOSIS; CANCER; QUANTIFICATION; GUIDELINES; ALGORITHM; PROSTATE; BIOPSY;

D O I：

10.1016/j.ijleo.2014.01.114

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Classification of liver masses is important to early diagnosis of patients. In this paper, a diagnostic system of liver disease classification based on contrast enhanced ultrasound (CEUS) imaging is proposed. In the proposed system, the dynamic CEUS videos of hepatic perfusion are firstly retrieved. Secondly, time intensity curves (TICs) are extracted from the dynamic CEUS videos using sparse non-negative matrix factorizations. Finally, deep learning is employed to classify benign and malignant focal liver lesions based on these TICs. Quantitative comparisons demonstrate that the proposed method outperforms the compared classification methods in accuracy, sensitivity and specificity. (C) 2014 Elsevier GmbH. All rights reserved.

引用

页码：4057 / 4063

页数：7

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