[Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models].
[Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models].
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DOI：
10.7507/1001-5515.201902011
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发表时间：
2019-12-25
期刊：
影响因子：
--
通讯作者：
Xin, Xiaojie
中科院分区：
文献类型：
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作者：
Xiong, Xiaoliang;Guo, Yi;Xin, Xiaojie
肾脏肿瘤是威胁人类健康的疾病之一。超声因其普及率高、价格低廉、无辐射等优点在肾脏肿瘤诊断中得到广泛应用。肾脏肿瘤的精确分割是精确治疗的基础。肾脏肿瘤常生长于皮质中部，分割易受邻近器官干扰。另外，超声图像对比度低，斑点噪声大，分割困难。提出了一种基于自适应子区域进化水平集模型（ASLSM）的超声图像肾脏肿瘤分割方法。首先将感兴趣区域划分为子区域。其次，综合内外能量和梯度设计目标函数，并自适应调整内外能量和梯度的比例。第三，ASLSM根据质心原理和零水平集内外的相似性来调整卷积半径和曲率。实验测得Hausdorff距离（HD）为（8.75 ± 4.21）mm，平均绝对距离（MAD）为（3.26 ± 1.69）mm，DICE为0.93 ± 0.03。与传统的超声分割方法相比，ASLSM对肾脏肿瘤的分割更加准确。ASLSM可为临床医生提供肾脏肿瘤定位诊断的方便。
Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.