2025/11/13 by Alomar Antonia, Rubio Ricardo, Antonia, Alomar +15
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Facial Rejuvenation and Surgery Techniques #Fetal and Pediatric Neurological Disorders
paper · pdf · doi:10.48550/arxiv.2511.10412
openalex publication_date 2025/11/13 · openalex created_date 2025/11/15 · openalex updated_date 2026/08/02
The automatic localization and standardization of anatomical planes in 3D medical imaging remains a challenging problem due to variability in object pose, appearance, and image quality. In 3D ultrasound, these challenges are exacerbated by speckle noise and limited contrast, particularly in fetal imaging. To address these challenges in the context of facial assessment, we present: 1) GT++, a robust algorithm that estimates standard facial planes from 3D US volumes using annotated anatomical landmarks; and 2) 3DFETUS, a deep learning model that automates and standardizes their localization in 3D fetal US volumes. We evaluated our methods both qualitatively, through expert clinical review, and quantitatively. The proposed approach achieved a mean translation error of 3.21 ± 1.98mm and a mean rotation error of 5.31 ± 3.945^∘ per plane, outperforming other state-of-the-art methods on 3D US volumes. Clinical assessments further confirmed the effectiveness of both GT++ and 3DFETUS, demonstrating statistically significant improvements in plane estimation accuracy.