Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation

被引:78
作者
Lee, Hansang [1 ]
Hong, Helen [2 ]
Kim, Junmo [1 ]
Jung, Dae Chul [3 ]
机构
[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, 291 Daehak Ro, Daejeon 34141, South Korea
[2] Seoul Womens Univ, Dept Software Convergence, Coll Interdisciplinary Studies Emerging Ind, 621 Hwarang Ro, Seoul 01797, South Korea
[3] Yonsei Univ, Coll Med, Dept Radiol, Severance Hosp,Res Inst Radiol Sci, 50-1 Yonsei Ro, Seoul 03722, South Korea
基金
新加坡国家研究基金会;
关键词
angiomyolipoma without visible fat (AMLwvf); clear cell renal cell carcinoma (ccRCC); computed tomography (CT); computer-aided diagnosis (CAD); deep feature classification; MINIMAL-FAT; POOR ANGIOMYOLIPOMA; UNENHANCED CT; DIFFERENTIATION; DIAGNOSIS;
D O I
10.1002/mp.12828
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. MethodsA dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. ResultsIn qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF+DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.61.4% for the proposed HCF+DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. ConclusionsThe proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images.
引用
收藏
页码:1550 / 1561
页数:12
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