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Ultrasound-based Phenotyping of Lateral Ventricles to Predict Hydrocephalus Outcome in Premature Neonates.

基于超声的侧脑室表型分型预测早产儿脑积水结局。

  • 影响因子:5.22
  • DOI:10.1109/TBME.2020.2974650
  • 作者列表:"Tabrizi PR","Mansoor A","Obeid R","Cerrolaza JJ","Perez DA","Zember J","Penn A","Linguraru MG
  • 发表时间:2020-02-18
Abstract

OBJECTIVE:Prediction of post-hemorrhagic hydrocephalus (PHH) outcome-i.e. whether it requires intervention or not-in premature neonates using cranial ultrasound (CUS) images is challenging. In this paper, we present a novel fully-automatic method to perform phenotyping of the brain lateral ventricles and predict PHH outcome from CUS. METHODS:Our method consists of two parts: ventricle quantification followed by prediction of PHH outcome. First, cranial bounding box and brain interhemispheric fissure are detected to determine the anatomical position of ventricles and correct the cranium rotation. Then, lateral ventricles are extracted using a new deep learning-based method by incorporating the convolutional neural network into a probabilistic atlas-based weighted loss function and an image-specific adaption. PHH outcome is predicted using a support vector machine classifier trained using ventricular morphological phenotypes and clinical information. RESULTS:Experiments demonstrated that our method achieves accurate ventricle segmentation results with an average Dice similarity coefficient of 0.86, as well as very good PHH outcome prediction with accuracy of 0.91. CONCLUSION:Automatic CUS-based ventricular phenotyping in premature newborns could objectively and accurately predict the progression to severe PHH. SIGNIFICANCE:Early prediction of severe PHH development in premature newborns could potentially advance criteria for diagnosis and offer an opportunity for early interventions to improve outcome.

摘要

目的: 预测出血性脑积水 (PHH) outcome-i.e。是否需要干预-在早产儿中使用头颅超声 (CUS) 图像具有挑战性。在本文中,我们提出了一种新的全自动方法来对脑侧脑室进行表型分析,并预测 CUS 的 PHH 结局。 方法: 我们的方法包括两部分: 心室定量,然后预测 PHH 结局。首先,检测颅骨包围盒和大脑半球间裂,以确定脑室的解剖位置并纠正颅骨旋转。然后,通过将卷积神经网络纳入基于概率图谱的加权损失函数和图像特异性适应,使用一种新的基于深度学习的方法提取侧脑室。使用使用心室形态表型和临床信息训练的支持向量机分类器预测 PHH 结局。 结果: 实验表明,我们的方法获得了准确的心室分割结果,平均 Dice 相似系数为 0.86,以及非常好的 PHH 结局预测,准确率为 0.91。 结论: 基于自动 CUS 的早产儿心室表型分析能客观、准确地预测进展为重度 PHH。 意义: 早期预测早产新生儿严重 PHH 发展可能会提高诊断标准,并为早期干预提供改善结局的机会。

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DOI:10.1007/s11011-020-00536-z
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影响因子:4.30
发表时间:2020-01-24
来源期刊:Experimental neurology
DOI:10.1016/j.expneurol.2020.113212
作者列表:["Bertrand SJ","Zhang Z","Patel R","O'Ferrell C","Punjabi NM","Kudchadkar SR","Kannan S"]

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影响因子:1.52
发表时间:2020-01-27
来源期刊:World neurosurgery
DOI:10.1016/j.wneu.2020.01.108
作者列表:["Middlebrooks EH","Lin C","Okromelidze L","Lu CQ","Tatum WO","Wharen RE Jr","Grewal SS"]

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