计算机科学
对比度(视觉)
三维超声
人工智能
分割
代表(政治)
噪音(视频)
边界(拓扑)
结构张量
张量(固有定义)
区域增长
尺度空间分割
水平集(数据结构)
图像分割
计算机视觉
活动轮廓模型
模式识别(心理学)
深度学习
超声波
图像(数学)
数学
放射科
几何学
医学
数学分析
政治
政治学
法学
作者
Jiahui Dong,Danni Ai,Jingfan Fan,Qiaoling Deng,Hong Song,Zhigang Cheng,Ping Liang,Yongtian Wang,Jian Yang
标识
DOI:10.1088/1361-6560/abfc92
摘要
Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.
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