In the past, improvements in craniofacial reconstructions (CFR) methodology languished due to the lack of adequate 3D databases that were sufficiently large and appropriate for 3-dimensional shape statistics. In our study, we created the "FACE-R" database from CT records and 3D surface scans of 400 clinical patients from Hungary, providing a significantly larger sample that was available before. The uniqueness of our database is linking of two data types that makes possible to investigate the bone and skin surface of the same individual, in upright position, thus eliminating many of the gravitational effects on the face during CT scanning. We performed a preliminary geometric morphometric (GMM) study using 3D data that produces a general idea of skull and face shape correlations. The vertical position of the tip of the (soft) nose for a skull and landmarks such as rhinion need to be taken into account. Likewise, the anterior nasal spine appears to exert some influence in this regard.
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients. An Attention U-Net is trained to output a probability density function of the BM position, while taking into account the natural curvature of the surface. Besides the surface position, the method also estimates an A-scan wise uncertainty measure of the segmentation output. Subsequently, the A-scans with high uncertainty are interpolated using thin plate splines (TPS). We tested our method with ablation studies on an internal dataset with 138 patients covering all three AMD stages, and achieved a mean absolute localization error of 4.10 µm. In addition, the proposed segmentation method was compared against the state-of-the-art methods and showed a superior performance on an external publicly available dataset from a different patient cohort and OCT device, demonstrating strong generalization ability.
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