Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada.
Department of Medicine, The Ottawa Hospital, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
Neuroinformatics. 2021 Oct;19(4):597-618. doi: 10.1007/s12021-021-09510-1. Epub 2021 Feb 1.
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N = 528 subjects for ICV, N = 501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer, DeepMedic), as well as two ventricular segmentation tools (FreeSurfer, DeepMedic). Our multi-contrast models outperformed other methods across many of the evaluation metrics, with average Dice coefficients of 0.98 and 0.96 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.
成功分割全脑颅腔 (ICV) 和脑室对于通过神经影像学研究神经退行性变至关重要。我们提出了 iCVMapper 和 VentMapper，这是两种强大的算法，它们使用卷积神经网络 (CNN) 从单对比度和多对比度 MRI 数据中分割 ICV 和脑室。我们的模型是在来自两个多中心研究的大型数据集上进行训练的（ICV 为 528 名受试者，脑室分割为 501 名），包括具有不同程度脑血管病变和萎缩的老年人，这对大多数分割方法构成了重大挑战。模型在 238 名参与者上进行了测试，包括患有血管性认知障碍和高脑白质高信号负荷的参与者。三个测试集中的两个来自未用于训练数据集的研究。我们相对于四种先进的 ICV 提取方法（MONSTR、BET、Deep Extraction、FreeSurfer、DeepMedic）以及两种脑室分割工具（FreeSurfer、DeepMedic）评估了我们的算法。我们的多对比度模型在许多评估指标上都优于其他方法，ICV 和脑室分割的平均 Dice 系数分别为 0.98 和 0.96。这两个模型也是最有效的，其分割结构的速度比其他一些可用方法快几个数量级。我们的网络在使用条件随机场 (CRF) 作为后处理步骤时表现出更高的准确性。我们进一步验证了这两种分割模型，与测试技术相比，它们能够稳健地处理分辨率和信噪比较低的图像。该流水线和模型可在以下网址获得：https://icvmapp3r.readthedocs.io 和 https://ventmapp3r.readthedocs.io，以促进对在大型多中心研究中 ICV 和脑室与正常衰老和神经退行性变之间关系的进一步研究。
