Barough Siavash Shirzadeh, Bilgel Murat, Ventura Catalina, An Lucas, Moghekar Ameya, Albert Marilyn S, Miller Michael I, Moghekar Abhay
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
medRxiv. 2025 Aug 21:2025.08.18.25333901. doi: 10.1101/2025.08.18.25333901.
Normal pressure hydrocephalus (NPH) is a potentially treatable neurodegenerative disorder that remains underdiagnosed due to its clinical overlap with other conditions and the labor-intensive nature of manual imaging analyses. Imaging biomarkers, such as the callosal angle (CA), Evans Index (EI), and Disproportionately Enlarged Subarachnoid Space Hydrocephalus (DESH), play a crucial role in NPH diagnosis but are often limited by subjective interpretations. To address these challenges, we developed a fully automated and robust deep learning framework for measuring the CA directly from raw T1 MPRAGE and non-MPRAGE MRI scans.
Our method integrates two complementary modules. First, a BrainSignsNET model is employed to accurately detect key anatomical landmarks, notably the anterior commissure (AC) and posterior commissure (PC). Preprocessed 3D MRI scans, reoriented to the Right Anterior Superior (RAS) system and resized to standardized cubes while preserving aspect ratios, serve as input for landmark localization. After detecting these landmarks, a coronal slice, perpendicular to the AC-PC line at the PC level, is extracted for subsequent analysis. Second, a UNet-based segmentation network, featuring a pretrained EfficientNetB0 encoder, generates multiclass masks of the lateral ventricles from the coronal slices which then used for calculation of the Callosal Angle.
Training and internal validation were performed using datasets from the Baltimore Longitudinal Study of Aging (BLSA) and BIOCARD, while external validation utilized 216 clinical MRI scans from Johns Hopkins Bayview Hospital. Our framework achieved high concordance with manual measurements, demonstrating a strong correlation (r = 0.98, p < 0.001) and a mean absolute error (MAE) of 2.95 (SD 1.58) degrees. Moreover, error analysis confirmed that CA measurement performance was independent of patient age, gender, and EI, underscoring the broad applicability of this method.
These results indicate that our fully automated CA measurement framework is a reliable and reproducible alternative to manual methods, outperforms reported interobserver variability in assessing the callosal angle, and offers significant potential to enhance early detection and diagnosis of NPH in both research and clinical settings.
正常压力脑积水（NPH）是一种潜在可治疗的神经退行性疾病，由于其与其他病症在临床上存在重叠，且手动影像分析需要耗费大量人力，故而仍未得到充分诊断。影像生物标志物，如胼胝体角（CA）、埃文斯指数（EI）和蛛网膜下腔不成比例扩大性脑积水（DESH），在NPH诊断中发挥着关键作用，但往往受到主观解读的限制。为应对这些挑战，我们开发了一种完全自动化且强大的深度学习框架，可直接从原始T1 MPRAGE和非MPRAGE MRI扫描中测量CA。
我们的方法整合了两个互补模块。首先，采用BrainSignsNET模型准确检测关键解剖标志，特别是前连合（AC）和后连合（PC）。预处理后的3D MRI扫描，重新定向到右前上（RAS）系统并调整为标准化立方体，同时保持纵横比，作为地标定位的输入。检测到这些地标后，在PC水平提取垂直于AC - PC线的冠状切片用于后续分析。其次，基于UNet的分割网络，具有预训练的EfficientNetB0编码器，从冠状切片生成侧脑室的多类掩码，然后用于计算胼胝体角。
使用来自巴尔的摩纵向衰老研究（BLSA）和BIOCARD的数据集进行训练和内部验证，而外部验证使用了约翰霍普金斯湾景医院的216例临床MRI扫描。我们的框架与手动测量高度一致，显示出强相关性（r = 0.98，p < 0.001），平均绝对误差（MAE）为2.95（标准差1.58）度。此外，误差分析证实CA测量性能与患者年龄、性别和EI无关，突出了该方法的广泛适用性。
这些结果表明，我们的全自动CA测量框架是一种可靠且可重复的手动方法替代方案，在评估胼胝体角方面优于报告的观察者间变异性，并在研究和临床环境中增强NPH早期检测和诊断方面具有巨大潜力。
