Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, China.
Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, China.
Biomed Res Int. 2017;2017:2059036. doi: 10.1155/2017/2059036. Epub 2017 Sep 10.
Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (), Jaccard (), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy ( = 0.9275 and = 0.8660 and FP = 0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.
超声断层成像（UST）图像分割是进行乳腺密度估计、药物反应分析和解剖变化量化的基础。现有的方法耗时且需要大量的手动交互。针对这些问题，本文提出了一种基于 GrabCut（AUGC）的自动算法。所提出的方法为不完整标记设计了自动化的 GrabCut 初始化，并通过多核并行编程加速。为了验证性能，AUGC 应用于三十二个体内 UST 体积图像的分割。使用乳腺重叠度量（Dice 系数（）、Jaccard（）和假阳性（FP））和时间成本（TC）验证 AUGC 的性能。此外，将 AUGC 与其他方法进行比较，包括置信度连通区域生长（CCRG）、分水岭和基于主动轮廓的曲线描绘（ACCD）。实验结果表明，AUGC 实现了最高的准确性（=0.9275 和=0.8660 和 FP=0.0077），平均处理一个体积图像约需 4 秒。有人说，AUGC 通过使用 UST 图像进行乳腺癌筛查和病理量化，为大规模研究带来了益处。
