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.

在通过神经影像学研究神经退行性变时，成功分割全颅内穹窿 (ICV) 和脑室至关重要。我们提出了 iCVMapper 和 VentMapper，它们是强大的算法，它们使用卷积神经网络 (CNN) 从单对比和多对比 MRI 数据中分割 ICV 和心室。我们的模型在来自两个多站点研究的大型数据集上进行了训练（ ICV 的N  = 528 名受试者，N = 501 用于心室分割）由具有不同程度脑血管病变和萎缩的老年人组成，这对大多数分割方法构成了重大挑战。这些模型在 238 名参与者身上进行了测试，包括患有血管性认知障碍和高白质高信号负荷的受试者。三个测试集中有两个来自训练数据集中未使用的研究。我们针对四种最先进的 ICV 提取方法（MONSTR、BET、Deep Extraction、FreeSurfer、DeepMedic）以及两种心室分割工具（FreeSurfer、DeepMedic）评估了我们的算法。我们的多对比模型在许多评估指标上都优于其他方法，ICV 和心室分割的平均 Dice 系数分别为 0.98 和 0.96。这两种模型也是最省时的，比其他一些可用方法更快地以数量级分割结构。通过使用条件随机场 (CRF) 作为后处理步骤，我们的网络显示出更高的准确性。我们进一步验证了这两种分割模型，与测试技术相比，强调了它们对具有较低分辨率和信噪比的图像的鲁棒性。管道和模型可在以下网址获得：https://icvmapp3r.readthedocs.io 和 https://ventmapp3r.readthedocs.io，以便进一步研究 ICV 和心室在大型多站点中与正常衰老和神经退行性变相关的作用学习。通过使用条件随机场 (CRF) 作为后处理步骤，我们的网络显示出更高的准确性。我们进一步验证了这两种分割模型，与测试技术相比，强调了它们对具有较低分辨率和信噪比的图像的鲁棒性。管道和模型可在以下网址获得：https://icvmapp3r.readthedocs.io 和 https://ventmapp3r.readthedocs.io，以便进一步研究 ICV 和心室在大型多站点中与正常衰老和神经退行性变相关的作用学习。通过使用条件随机场 (CRF) 作为后处理步骤，我们的网络显示出更高的准确性。我们进一步验证了这两种分割模型，与测试技术相比，强调了它们对具有较低分辨率和信噪比的图像的鲁棒性。管道和模型可在以下网址获得：https://icvmapp3r.readthedocs.io 和 https://ventmapp3r.readthedocs.io，以便进一步研究 ICV 和心室在大型多站点中与正常衰老和神经退行性变相关的作用学习。