An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI
An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI
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DOI：
10.1016/j.neuroimage.2019.116324
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发表时间：
2020-02-01
期刊：
影响因子：
5.7
通讯作者：
Vercauteren, Tom
中科院分区：
文献类型：
--
作者：
Ebner, Michael;Wang, Guotai;Vercauteren, Tom
从多个运动受损的2D切片层叠进行高分辨率体积重建在胎儿脑磁共振成像(MRI)研究中发挥着越来越重要的作用。目前，现有的重建方法是耗时的，并且通常需要用户交互来定位并从几叠2D切片中提取大脑。提出了一种全自动胎儿脑重建框架，该框架包括四个阶段：1)基于卷积神经网络(CNN)粗分割的胎儿脑定位；2)另一种基于多尺度损失函数训练的CNN的精细分割；3)新颖的单参数离群点稳健超分辨率重建；4)适合于病理性脑的标准解剖空间的快速自动高分辨率可视化。我们用胎儿的图像验证了我们的框架，这些胎儿的大脑正常，脑室不同程度的扩大与开放性脊柱裂相关，这是一种也影响大脑的先天性畸形。实验表明，我们提出的流水线的每一步在分割和重建比较(包括专家-阅读器质量评估)方面都优于最先进的方法。我们提出的方法的重建结果与人工、劳动密集型脑分割获得的结果相比是有利的，这释放了自动胎脑重建研究在临床实践中的潜在用途。
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice.