Exploiting 3d Ultrasound for Fetal Diagnostic Purpose Through Facial Landmarking

Abstract

In the last decade, three-dimensional landmarking has gained attention for different applications, such as face recognition for both identification of suspects and authentication, facial expression recognition, corrective and aesthetic surgery, syndrome study and diagnosis. This work focuses on the last one by proposing a geometrically-based landmark extraction algorithm aimed at diagnosing syndromes on babies before their birth. Pivotal role in this activity is the support provided by physicians and 3D ultrasound tools for working on real faces. In particular, the landmarking algorithm here proposed only relies on descriptors coming from Differential Geometry (Gaussian, mean, and principal curvatures, derivatives, coefficients of first and second fundamental forms, Shape and Curvedness indexes) and is tested on nine facial point clouds referred to nine babies taken by a three-dimensional ultrasound tool at different weeks' gestation. The results obtained, validated with the support of four practitioners, show that the localization is quite accurate. All errors lie in the range between 0 and 3.5 mm and the mean distance for each shell is in the range between 0.6 and 1.6 mm. The landmarks showing the highest errors are the ones belonging to the mouth region. Instead, the most precise landmark is the pronasal, on the nose tip, with a mean distance of 0.55 mm. Relying on current literature, this study is something missing in the state-of-the-art of the field, as present facial studies on 3D ultrasound do not work on automatic landmarking yet.

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

“…In addition, the automatic recognition of facial expressions has resulted in applications that span several disciplines. For example, facial expressions are used to identify pain [29,30], diagnose syndromes in infants [31,32], detect driver's drowsiness [33][34][35], recognize emotions [36][37][38][39][40], and detect engagement [41,42].…”

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

“…Automatic detection of facial landmarks has led to the development of applications that cut across different domains. Examples include where facial expression serves as intelligent systems for pain detection [ 21 ], syndrome diagnosis in newborns [ 22 ], driver’s drowsiness [ 23 ], emotion recognition [ 24 ], and engagement detection [ 2 , 25 ]. The state-of-art of face-based attention or engagement recognition is usually centered on three stages: face detection (data collection and exploration), facial feature extraction, and classification [ 26 ].…”

Automatic vocal tract landmark localization from midsagittal MRI data

“…This short review emphasizes the importance of anatomical landmark localization from images for a large variety of applications and this study lengthens this non-exhaustive list with speech production.Within the existing literature, two fields of application appear more particularly active and connected to our problem. The first is the localization of landmarks for the face, rich of an abundant research from landmark identification on two-dimensional photographs 23,24 to landmark identification on three-dimensional Ultrasounds for fetuses for instance 25,26 . A recent and comprehensive review is provided by Wu et al 27 .…”