Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.
文献类型：期刊论文
| 作者 | Yu, Yanyan; Cheng, Jieyu; Chiu, Bernard; Xiao, Yang |
| 刊名 | COMPUTERS IN BIOLOGY AND MEDICINE
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Supersonic shear-wave elastography (SWE) has emerged as a useful imaging modality for breastlesion assessment. Regions of interest (ROIs) were required to be specified for extracting features that characterize malignancy of lesions. Although analyses have been performed in small rectangular ROIs identified manually by expert observers, the results were subject to observer variability and the analysis of small ROIs would potentially miss out important features available in other parts of the lesion. Recent investigations extracted features from the entire lesion segmented by B-modeultrasound images either manually or semi-automatically, but lesion delineation using existing techniques is time-consuming and prone to variability as intensive user interactions are required. In addition, rich diagnostic features were available along the rim surrounding the lesion. The width of the rim analyzed was subjectively and empirically determined by expert observers in previous studies after intensive visual study on the images, which is time-consuming and susceptible to observer variability. This paper describes an analysis pipeline to segment and classify lesions efficiently. The lesionboundary was first initialized and then deformed based on energy fields generated by the dyadic wavelet transform. Features of the SWE images were extracted from inside and outside of a lesion for different widths of the surrounding rim. Then, feature selection was performed followed by the Support Vector Machine (SVM) classification. This strategy obviates the empirical and time-consuming selection of the surrounding rim width before the analysis. The pipeline was evaluated on 137 lesions. Feature selection was performed 20 times using different sets of 14 lesions (7 malignant, 7 benign). Leave-one-out SVM classification was performed in each of the 20 experiments with a mean sensitivity, specificity and accuracy of 95.1%, 94.6% and 94.8% respectively. The pipeline took an average of 20 s to process a lesion. The fact that this efficient pipeline generated classificationaccuracy superior to that of existing algorithms suggests that improved efficiency did not compromise classification accuracy. The ability to streamline the quantitative assessment of SWE images will potentially accelerate the adoption of the combined use of ultrasound and elastography in clinical practice. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14308]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Yu, Yanyan,Cheng, Jieyu,Chiu, Bernard,et al. Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2018. |
| APA | Yu, Yanyan,Cheng, Jieyu,Chiu, Bernard,&Xiao, Yang.(2018).Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images..COMPUTERS IN BIOLOGY AND MEDICINE. |
| MLA | Yu, Yanyan,et al."Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.".COMPUTERS IN BIOLOGY AND MEDICINE (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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