BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Med Image Anal. 2019 May;54:263-279. doi: 10.1016/j.media.2019.03.008. Epub 2019 Mar 28.
Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25-37 weeks of gestation. Dice coefficients of 0.82 ± 0.02 and 0.81 ± 0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders.
胎儿磁共振成像（MRI）的最新进展为提高胎儿和胎盘异常的检测和特征描述能力开辟了道路。由于解释 MRI 数据可能比较复杂和模糊，因此需要强大的计算方法来定量分析胎盘的解剖结构（包括其血管）和功能。在这项工作中，我们提出了一种新颖的全自动方法，从胎儿 MRI 中分割胎盘及其周围血管。首先，通过组合轴位、矢状位和冠状位视图生成子宫的超分辨率重建。然后，使用 3D 伽柏滤波器、纹理特征和支持向量机对胎盘进行分割。提出了一种基于子宫边缘的实例选择方法，以识别定义胎盘边界的支持向量。随后，通过基于曲率的角检测器提取外周血管。我们的方法在一个丰富的 44 个对照和病理病例集上进行了验证：25-37 周龄的单胎和（正常/单绒毛膜）双胞胎妊娠。胎盘和其血管分割的 Dice 系数分别达到 0.82±0.02 和 0.81±0.08。还对最先进的卷积神经网络（CNN），即 3D U-Net、V-Net、DeepMedic、Holistic3D Net、HighRes3D Net 和 Dense V-Net 进行了胎盘定位的对比分析，我们的方法优于所有 CNN 方法。结果表明，我们的方法可以辅助严重胎儿疾病的诊断和手术规划。
