Lee Soojin, Rieu ZunHyan, Kim Regina Ey, Lee Minho, Yen Kevin, Yong Junghyun, Kim Donghyeon
Research Institute, NEUROPHET Inc., Seoul, South Korea; Pacific Parkinson's Research Centre, The University of British Columbia, Vancouver, Canada.
Research Institute, NEUROPHET Inc., Seoul, South Korea.
Brain Res Bull. 2023 Dec;205:110825. doi: 10.1016/j.brainresbull.2023.110825. Epub 2023 Nov 22.
White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.
脑白质高信号（WMHs）是大脑白质中的病变，与认知能力下降和痴呆风险增加有关。WMHs的手动分割非常耗时，并且容易出现内部和外部的变异性。因此，自动分割方法作为一种更有效、更客观的检测和监测WMHs的手段正受到关注。在本研究中，我们提出了AQUA，这是一种深度学习模型，旨在从T2-FLAIR扫描中对WMHs进行全自动分割，它改进了我们之前关于小病变检测的研究，并采用了多中心方法。AQUA实现了二维U-Net架构，并使用基于补丁的训练。此外，该网络经过修改，在编码器和解码器的每个卷积块上都包含瓶颈注意力模块，以提高对小尺寸WMHs的分割性能。我们通过将AQUA与五种用于WMHs自动分割的知名监督和无监督方法（LGA、LPA、SLS、UBO和BIANCA）进行比较，评估了AQUA的性能和鲁棒性。为此，我们在MICCAI 2017 WMH分割挑战赛数据集上测试了这六种方法，该数据集包含来自170名患有推测为血管源性WMHs的老年参与者的MRI图像，并评估了它们在多个站点和扫描仪类型上的鲁棒性。结果表明，与其他方法相比，AQUA在与手动分割的空间（Dice = 0.72）和体积（logAVD = 0.10）一致性方面表现出色。虽然召回率和F1分数分别为中等水平，即0.49和0.59，但在排除小病变（≤6体素）时，它们分别提高到了0.75和0.82。值得注意的是，尽管AQUA是在具有不同种族背景、病变负荷和扫描仪的不同数据集上进行训练的，但其结果与MICCAI挑战赛排名前十的方法相当。研究结果表明，AQUA对于从T2-FLAIR扫描中自动分割WMHs是有效且实用的，这有助于识别有认知能力下降和痴呆风险的个体，并实现早期干预和管理。
