3D Facial Landmark Localization for cephalometric analysis.

Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models. Clinical Relevance- Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis.

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities.

Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.

Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability.

Shape analysis of infant's heads is crucial to diagnose cranial deformities and evaluate head growth. Currently available 3D imaging systems can be used to create 3D head models, promoting the clinical practice for head evaluation. However, manual analysis of 3D shapes is difficult and operator-dependent, causing inaccuracies in the analysis. This study aims to validate an automatic landmark detection method for head shape analysis. The detection results were compared with manual analysis in three levels: (1) distance error of landmarks; (2) accuracy of standard cranial measurements, namely cephalic ratio (CR), cranial vault asymmetry index (CVAI), and overall symmetry ratio (OSR); and (3) accuracy of the final diagnosis of cranial deformities. For each level, the intra- and interobserver variability was also studied by comparing manual landmark settings. High landmark detection accuracy was achieved by the method in 166 head models. A very strong agreement with manual analysis for the cranial measurements was also obtained, with intraclass correlation coefficients of 0.997, 0.961, and 0.771 for the CR, CVAI, and OSR. 91% agreement with manual analysis was achieved in the diagnosis of cranial deformities. Considering its high accuracy and reliability in different evaluation levels, the method showed to be feasible for use in clinical practice for head shape analysis.

Reference Charts for Neonatal Cranial Volume Based on 3D Laser Scanning to Monitor Head Growth.

Background: Postnatal brain growth is an important predictor of neurodevelopmental outcome in preterm infants. A new reliable proxy for brain volume is cranial volume, which can be measured routinely by 3-D laser scanning. The aim of this study was to develop reference charts for normal cranial volume in newborn infants at different gestational ages starting from late preterm for both sexes. Methods: Cross-sectional cohort study in a German university hospital, including singleton, clinically stable, neonates born after 34 weeks of gestation. Cranial volume was measured in the first week of life by a validated 3-D laser scanner. Cranial volume data was modeled to calculate percentile values by gestational age and birth weight and to develop cranial volume reference charts for girls and boys separately. Results: Of the 1,703 included infants, 846 (50%) were female. Birth weights ranged from 1,370 to 4,830 grams (median 3,370). Median cranial volume ranged from 320 [interquartile range (IQR) 294-347] ml at 34 weeks to 469 [IQR 442-496] ml at 42 weeks and was higher in boys than in girls. Conclusions: This study presents the first reference charts of cranial volume which can be used in clinical practice to monitor brain growth between 34 and 42 weeks gestation in infants.

Obstetric forceps dimensions and the newborn head biometry: Time for an update.

Obstetric forceps were invented in the 1600s to assist vaginal delivery of term babies following prolonged labour. This probably explains their design, with a narrow interblade distance and long blade length, to fit a severely moulded fetal head. However, in modern obstetric practice protracted labour is avoided, yet our research has shown that over 400 years forceps dimensions have remained largely unchanged. We believe it is time to optimise these dimensions based on biometry of the term, newborn baby's head, with the head width (biparietal diameter) and head length (mentovertical diameter) correlating with interblade distance and blade length respectively. We hypothesise that doing so should reduce the incidence of neonatal complications associated with forceps assisted delivery and it is also possible that the amended shape might be associated with better outcomes for women. In this article we present our rationale for the optimisation of the forceps dimensions based on the findings of our previous systematic review and an original series of mentovertical and biparietal diameter measurements using laser scanning technology.

Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach.

Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant's head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method's performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis.