Department of Biomedical Engineering, Ankara University, Ankara, Turkey; Department of Interdisciplinary Neuroscience, Health Science Institute, Ankara University, Ankara, Turkey.
Department of Biomedical Engineering, Ankara University, Ankara, Turkey.
J Neuroradiol. 2022 Sep;49(5):364-369. doi: 10.1016/j.neurad.2021.02.001. Epub 2021 Feb 12.
Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus.
We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms.
The dataset contains 61 True-fisp data with routine sequences 37 of which are labeled as 'hydrocephalus' and the others as 'normal condition'. A fifteen-year experienced neuroradiologist divided data into two groups. The first group, 'hydrocephalus', consists of patients with typical MRI findings (ventriculomegaly, enlargement of the third ventricular recesses and lateral ventricular horns, decreased mamillo-pontine distance, reduced frontal horn angle, thinning/elevation of the corpus callosum, and non-dilated convexity sulci), and the second group contains samples that did not show any symptoms or neurologic abnormality and labeled as 'normal condition'. The region of interest was determined by the radiologist supervisor to cover the LT. To achieve our purpose, we used both spatial and spatio-temporal analysis with two different deep learning architectures. We utilized Convolutional Neural Networks (CNN) for spatial and Convolutional Long Short-Term Memory (ConvLSTM) models for spatio-temporal analysis using an ROI around LT on sagittal True-fisp images.
Our results show that 80.7% classification accuracy was achieved with the ConvLSTM model exploiting LT motion, whereas 76.5% and 71.6% accuracies were obtained by the 2D CNN model using all frames, and only the first frame from only spatial information, respectively.
We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.
对于治疗脑积水的 CSF 分流的非侵入性评估，终板（LT）的评估至关重要。结合深度学习算法，LT 的形态和生理学分析可能在脑积水的管理中发挥重要作用。
我们旨在展示利用 LT 的运动可以通过深度学习算法来帮助评估脑积水。
该数据集包含 61 个 True-fisp 数据，其中常规序列为 37 个，标记为“脑积水”，其余为“正常状态”。一位拥有 15 年经验的神经放射科医生将数据分为两组。第一组“脑积水”包括具有典型 MRI 表现的患者（脑室扩大、第三脑室隐窝和侧脑室角扩大、鞍结节距离减小、额角减小、胼胝体变薄/抬高、凸面脑沟无扩张），第二组包含没有任何症状或神经异常的样本，标记为“正常状态”。感兴趣区域由放射科医生主管确定，以覆盖 LT。为了达到我们的目的，我们使用了两种不同的深度学习架构进行空间和时空分析。我们使用卷积神经网络（CNN）进行空间分析，使用卷积长短期记忆（ConvLSTM）模型进行时空分析，在矢状 True-fisp 图像上围绕 LT 进行 ROI。
我们的结果表明，使用 LT 运动的 ConvLSTM 模型可实现 80.7%的分类准确性，而仅使用所有帧的 2D CNN 模型和仅使用空间信息的第一帧分别可获得 76.5%和 71.6%的准确性。
我们建议 LT 的运动可以作为空间信息的附加属性用于评估脑积水。
