Automated Measurement of Brainstem-Vermis and Brainstem-Tentorium Angles in Fetal MRI Images Based on Landmark Detection

The Brainstem-Vermis (BV) and BrainstemTentorium (BT) angles in the fetal head are two essential indicators for determining whether the fetal development is normal. If these two angle values deviate from the norm, there is a high likelihood of potential fetal health issues arising. Traditional manual measurement is not only time-consuming but also prone to errors. Previous CNN-based landmark detection methods have exhibited some effectiveness. However, These methods for angle calculation using landmarks often overlook their inherent directionality, and their network structures struggle to adapt to the significant variations in fetal size observed in medical images. In this paper, we propose a two-layer neural network and heatmaps with anisotropic Gaussian functions. Specifically, the network extracts local and global features and fuse them together. The global feature attends to the more critical regions in the image, thus correcting ambiguous areas within the local features. Moreover, we introduce heatmaps based on anisotropic Gaussian functions to utilize the direction information associated with landmarks to impose directional constraints on the network’s predictions, resulting in enhanced accuracy in angle calculations. We collected a dataset of 493 fetus MRI images to train and evaluate the performance of the proposed model. Experimental results demonstrate that our method achieves state-of-the-art performance.