Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
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DOI：
10.3171/2020.6.peds20251
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发表时间：
2021-02-01
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脑室的影像学评价对小儿脑积水的临床决策具有重要意义。虽然随着时间的推移，心室大小的定量测量可以促进客观比较，但用于计算心室容积的自动化工具并不适合临床使用。我们的目标是开发一种用于儿科脑室分割和体积计算的全自动深度学习（DL）模型，以便在多家医院进行广泛的临床实施。该研究队列包括来自四家儿科医院的200名患有阻塞性脑积水的儿童和199名对照。手动心室分割和体积计算作为基础事实。编码器-解码器卷积神经网络（CNN）架构使用T2加权MRI作为输入，自动描绘心室并输出体积测量值。在保留的测试集上，使用Dice相似系数（0至1）和使用线性回归的体积计算评估分割准确度。在第五家医院的外部MRI数据集上评价了模型的可推广性。将DL模型性能与研究分割软件FreeSurfer（FS）进行比较。进行模型分割，总体Dice评分为0.901（脑积水为0.946，对照为0.856）。该模型推广到第五家儿科医院的外部MRI，Dice评分为0.926。该模型比FS更准确，操作时间更快（每次扫描1.48秒）。我们提出了一个DL模型自动心室分割和体积计算，是更准确和更快的比目前可用的方法。我们的模型具有接近即时的体积输出和跨机构扫描仪类型的可靠性能，可适用于脑积水的实时临床评估，并改善临床医生的工作流程。
Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. While quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. We aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals and 199 controls. Manual ventricle segmentation and volume calculation served as ground truth. An encoder-decoder convolutional neural network (CNN) architecture, using T2-weighted MRIs as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using Dice similarity coefficient (0 to 1) and volume calculation using linear regression. Model generalizability was evaluated on an external MRI dataset from a fifth hospital. DL model performance was compared against FreeSurfer (FS), research segmentation software. Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MRIs from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FS, with faster operating times (1.48 seconds per scan). We present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than current available methods. With near immediate volumetric output and reliable performance across institutional scanner types, our model can be adapted to the real-time, clinical evaluation of hydrocephalus and improve clinician workflow.