Bawil Mahdi Bashiri, Shamsi Mousa, Bavil Abolhassan Shakeri, Danishvar Sebelan
Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.
Radiology Department, Tabriz University of Medical Sciences, Tabriz, Iran.
Front Neurosci. 2024 Sep 30;18:1416174. doi: 10.3389/fnins.2024.1416174. eCollection 2024.
White matter hyperintensities (WMH) observed in T2 fluid-attenuated inversion recovery (FLAIR) images have emerged as potential markers of neurodegenerative diseases like Multiple Sclerosis (MS). Lacking comprehensive automated WMH classification systems in current research, there is a need to develop accurate detection and classification methods for WMH that will benefit the diagnosis and monitoring of brain diseases.
Juxtacortical WMH (JCWMH) is a less explored subtype of WMH, primarily due to the hard definition of the cortex in FLAIR images, which is escalated by the presence of lesions to obtain appropriate gray matter (GM) masks.
In this study, we present a method to perform a specialized GM segmentation developed for the classification of WMH, especially JCWMH. Using T1 and FLAIR images, we propose a pipeline to integrate masks of white matter, cerebrospinal fluid, ventricles, and WMH to create a unique mask to refine the primary GM map. Subsequently, we utilize this pipeline to generate paired data for training a conditional generative adversarial network (cGAN) to substitute the pipeline and reduce the inputs to only FLAIR images. The classification of WMH is then based on the distances between WMH and ventricular and GM masks. Due to the lack of multi-class labeled WMH datasets and the need for extensive data for training deep learning models, we attempted to collect a large local dataset and manually segment and label some data for WMH and ventricles.
In JCWMH classification, the proposed method exhibited a Dice similarity coefficient, precision, and sensitivity of 0.76, 0.69, and 0.84, respectively. With values of 0.66, 0.55, and 0.81, the proposed method clearly outperformed the approach commonly used in the literature, which uses extracted GM masks from registered T1 images on FLAIR.
After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.
在T2液体衰减反转恢复（FLAIR）图像中观察到的白质高信号（WMH）已成为多发性硬化症（MS）等神经退行性疾病的潜在标志物。由于当前研究中缺乏全面的自动WMH分类系统，因此需要开发准确的WMH检测和分类方法，这将有助于脑部疾病的诊断和监测。
皮质下白质高信号（JCWMH）是一种研究较少的WMH亚型，主要是因为FLAIR图像中皮质的定义困难，而病变的存在又加剧了这种困难，从而难以获得合适的灰质（GM）掩码。
在本研究中，我们提出了一种用于WMH分类（特别是JCWMH分类）的专门GM分割方法。利用T1和FLAIR图像，我们提出了一个流程，整合白质、脑脊液、脑室和WMH的掩码，以创建一个独特的掩码来细化原始GM图谱。随后，我们利用这个流程生成配对数据，用于训练条件生成对抗网络（cGAN），以替代该流程并将输入减少到仅FLAIR图像。然后，基于WMH与脑室和GM掩码之间的距离对WMH进行分类。由于缺乏多类标记的WMH数据集，且训练深度学习模型需要大量数据，我们试图收集一个大型本地数据集，并手动分割和标记一些WMH和脑室的数据。
在JCWMH分类中，所提出的方法的Dice相似系数、精度和灵敏度分别为0.76、0.69和0.84。所提出的方法的值分别为0.66、0.55和0.81，明显优于文献中常用的方法，即使用从FLAIR上注册的T1图像中提取的GM掩码的方法。
经过训练，该方法在不到一秒的时间内给出结果，证明了其效率。相比之下，通常的方法仅注册和分割就至少需要两分钟。所提出的方法是自动化且快速的，并且不需要初始化，因为它仅使用FLAIR图像。这种创新方法无疑将通过降低复杂性和提高效率，促进临床实践中对WMH进行准确且有意义的分析。
