Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada.
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
Med Image Anal. 2017 Jan;35:181-191. doi: 10.1016/j.media.2016.06.038. Epub 2016 Jul 9.
Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images.
出生体重极低（<1500 克）的早产儿患脑室出血（IVH）的风险增加。IVH 患者的脑室进行性扩张可能导致颅内压升高，从而导致神经损伤，如神经发育迟缓或脑瘫。三维超声（US）成像技术已用于定量监测 IVH 新生儿的脑室容积，这可能阐明了 2D 临床 US 成像依赖于对一系列 2D 切片进行脑室扩张的线性测量和视觉估计的情况下，这些患者的干预时机存在的不确定性。为了将 3D US 成像转化为临床环境，需要一种全自动的分割算法来从 3D 新生儿脑 US 图像中提取脑室系统。本文提出了一种自动分割方法，用于从 3D US 图像中描绘早产儿的侧脑室。所提出的分割方法利用相位一致性图、多图谱初始化技术、图谱选择策略以及多相位测地线水平集（MGLS）演化，结合从多个预分割图谱中得出的空间形状先验。使用 30 个 IVH 患者图像的实验结果表明，所提出的 GPU 实现方法在骰子相似系数（DSC）、平均绝对表面距离（MAD）和最大绝对表面距离（MAXD）方面具有较高的准确性。据我们所知，本文首次报道了从 3D US 图像中自动分割早产儿脑脑室系统的研究。
