Abstract：
BACKGROUND: Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging.
METHODS: We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition.
RESULTS: The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression.
CONCLUSIONS: Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.
METHODS: We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition.
RESULTS: The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression.
CONCLUSIONS: Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.
摘要：
背景：CADASIL中白质高强度（WMH）的分割，遗传起源的最严重的脑小血管病之一，具有挑战性。
方法：我们调整并验证了一种基于卷积神经网络（CNN）算法的自动方法，并使用了在132名患者中采集的大型2D和/或3DFLAIR和T1加权图像数据集，在这种情况下测量WMH的进展。
结果：使用此方法测得的WMH体积与专家验证的参考数据密切相关。与BIANCA分割方法相比，WMH分割也得到了明显改善。结合两个连续的学习模型被发现是特别感兴趣的，减少假阳性体素的数量和单阶段过程后检测到的分割不足的程度。通过两阶段方法，WMH进展与从参考面罩获得的随年龄增加的病变测量值相关，并且在个体水平上具有可变的WMH进展轨迹。我们还证实了WMH的初始负荷的预期效果以及MRI采集类型对这种进展的测量的影响。
结论：总而言之，我们的研究结果表明,CADASIL中的WMH进展可以通过CNN分割算法以足够的置信度自动测量.
方法：我们调整并验证了一种基于卷积神经网络（CNN）算法的自动方法，并使用了在132名患者中采集的大型2D和/或3DFLAIR和T1加权图像数据集，在这种情况下测量WMH的进展。
结果：使用此方法测得的WMH体积与专家验证的参考数据密切相关。与BIANCA分割方法相比，WMH分割也得到了明显改善。结合两个连续的学习模型被发现是特别感兴趣的，减少假阳性体素的数量和单阶段过程后检测到的分割不足的程度。通过两阶段方法，WMH进展与从参考面罩获得的随年龄增加的病变测量值相关，并且在个体水平上具有可变的WMH进展轨迹。我们还证实了WMH的初始负荷的预期效果以及MRI采集类型对这种进展的测量的影响。
结论：总而言之，我们的研究结果表明,CADASIL中的WMH进展可以通过CNN分割算法以足够的置信度自动测量.
