Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model
Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model
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DOI：
10.1080/21681163.2015.1029642
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发表时间：
2017-01-01
影响因子：
1.6
通讯作者：
Makris, Dimitrios
中科院分区：
文献类型：
--
作者：
Bakas, Spyridon;Chatzimichail, Katerina;Makris, Dimitrios
在超声造影中评估肝脏局灶性病变需要在至少一帧采集的数据中勾画肝脏病变，目前这是由经验丰富的放射科医生手动完成的。这样的任务会导致主观结果，耗时长，容易产生误解和人为错误。本文描述了一种改进这一临床实践的尝试，提出了一种新的快速两步法来自动化FLL分割，仅由单个种子点初始化。首先，采用矩形力函数来提高有源椭圆模型的精度和计算效率。然后，使用一种新的概率边界细化方法对边界像素进行迭代快速分类。建议的方法允许更快和更容易地评估FL，同时需要较少的交互，但产生的结果与手动描述相一致，从而增加了放射科医生在做出诊断时的信心。基于来自两个不同欧洲国家的真实临床数据的定量评估，反映了真实的临床实践，证明了所提出的方法的价值。
Assessment of focal liver lesions (FLLs) in contrast-enhanced ultrasound requires the delineation of the FLL in at least one frame of the acquired data, which is currently performed manually by experienced radiologists. Such a task leads to subjective results, is time-consuming and prone to misinterpretation and human error. This paper describes an attempt to improve this clinical practice by proposing a novel fast two-step method to automate the FLL segmentation, initialised only by a single seed point. Firstly, rectangular force functions are used to improve the accuracy and computational efficiency of an active ellipse model for approximating the FLL shape. Then, a novel probabilistic boundary refinement method is used to iteratively classify boundary pixels rapidly. The proposed method allows for faster and easier assessment of FLLs, whilst requiring less interaction, but producing results comparably consistent with manual delineations, and hence increasing the confidence of radiologists when making a diagnosis. Quantitative evaluation based on real clinical data, from two different European countries reflecting true clinical practice, demonstrates the value of the proposed method.