Rueda Sylvia, Knight Caroline L, Papageorghiou Aris T, Noble J Alison
Centre of Excellence in Personalised Healthcare, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, OX3 7DQ Oxford, UK.
Centre of Excellence in Personalised Healthcare, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, OX3 7DQ Oxford, UK; Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, U.K.
Med Image Anal. 2015 Dec;26(1):30-46. doi: 10.1016/j.media.2015.07.002. Epub 2015 Jul 17.
Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice.
医学超声（US）图像的分割和量化可能具有挑战性，这是因为存在信号丢失、边界缺失以及斑点，这些因素使得相似物体的图像呈现出相当不同的外观。通常，单纯基于强度的方法无法对感兴趣的结构进行良好的分割。先前的研究表明，从单基因信号中导出的局部相位和特征不对称性能够从超声图像中提取结构信息。本文提出了一种基于模糊连通性框架的新型超声分割方法。该方法使用局部相位和特征不对称性来定义一种新颖的亲和函数，该函数驱动分割算法，纳入基于形状的目标完成步骤，并通过平均曲率流对结果进行正则化。为了评估该方法在不同外观和质量的临床数据中的准确性和鲁棒性，引入了一种基于熵的新型感兴趣区域定量图像质量评估方法。新方法应用于多个孕周获取的81幅胎儿手臂超声图像，以此来定义一种基于图像的新型胎儿营养自动生物标志物。定量和定性评估表明，该分割方法与手动勾勒相当，并且在临床实践中典型的图像质量范围内具有鲁棒性。
