An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

Preoperative Virtual Reduction Planning Algorithm of Fractured Pelvis Based on Adaptive Templates

OBJECTIVE

The minimally invasive treatment of pelvic fractures is one of the most challenging trauma orthopedics surgeries, where preoperative planning is crucial for the performance and outcome of the surgery. However, planning the ideal position of fragments currently relies heavily on the experience of the surgeon.

METHODS

A pelvic fracture virtual reduction algorithm for target position is provided based on statistical shape models (SSM). First, according to sexual dimorphism, pelvic SSM based on point cloud curvature down-sampling are constructed as adaptive templates. Then, an optimization algorithm is designed to iteratively adjust the target pose of the fragments and the adaptive matching of the templates. Finally, the feasibility of the method is verified by simulating fractures and clinical data.

RESULTS

The pelvis has complex shape characteristics, which can be analyzed by SSM to clearly understand the pattern of change. Experiments showed that the SSM-based pelvic fracture reduction method had translation and rotation errors of 2.20±1.09 mm and 3.16±1.26° in simulated cases, and 2.78±0.95 mm and 3.10±0.53° in clinical cases, which has higher accuracy than methods based on mean shape models, and wider applicability than methods based on pelvic symmetry.

CONCLUSION

The pelvic digital model created by SSM has good generalization properties, and the SSM-based virtual reduction algorithm can effectively reconstruct the target position of the fractured pelvis in preoperative planning.

SIGNIFICANCE

The proposed reduction method has the characteristics of high precision and wide application range, which provides a powerful tool for the surgeon's virtual preoperative planning.

Modeling 3D geometric growth patterns and variations of Children's heads

To design high-quality head/face-related products for children, it is essential to be able to construct 3D geometric models of their head growth patterns and variations. However, compared to 3D anthropometric analysis of adults' heads, this is still an underexplored research area. This study developed a framework for modeling the 3D geometric growth patterns and sex-specific variations of children's heads. To analyze these variations, the entire heads of 793 children (395 females and 398 males) ages 5-17 were scanned, and one global and two sex-specific statistical shape models (SSMs) were constructed. The first principal component in these SSMs, contributing more than 65% to the total explained variations, was highly related to overall head sizes. To model growth patterns, expected average heads for different ages and per-vertex growth rates were computed. Our results showed that the entire female head basically reaches its mature size at age 13-14, whereas in males it continues to increase until age 16-17. This study therefore provides valuable references for children's head/face-related product design, including the development of a more accurate sizing system and improvements in product fit and function.

Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template

(1) Background: Three-dimensional (3D) facial anatomical landmarks are the premise and foundation of facial morphology analysis. At present, there is no ideal automatic determination method for 3D facial anatomical landmarks. This research aims to realize the automatic determination of 3D facial anatomical landmarks based on the non-rigid registration algorithm developed by our research team and to evaluate its landmark localization accuracy. (2) Methods: A 3D facial scanner, Face Scan, was used to collect 3D facial data of 20 adult males without significant facial deformities. Using the radial basis function optimized non-rigid registration algorithm, TH-OCR, developed by our research team (experimental group: TH group) and the non-rigid registration algorithm, MeshMonk (control group: MM group), a 3D face template constructed in our previous research was deformed and registered to each participant’s data. The automatic determination of 3D facial anatomical landmarks was realized according to the index of 32 facial anatomical landmarks determined on the 3D face template. Considering these 32 facial anatomical landmarks manually selected by experts on the 3D facial data as the gold standard, the distance between the automatically determined and the corresponding manually selected facial anatomical landmarks was calculated as the “landmark localization error” to evaluate the effect and feasibility of the automatic determination method (template method). (3) Results: The mean landmark localization error of all facial anatomical landmarks in the TH and MM groups was 2.34 ± 1.76 mm and 2.16 ± 1.97 mm, respectively. The automatic determination of the anatomical landmarks in the middle face was better than that in the upper and lower face in both groups. Further, the automatic determination of anatomical landmarks in the center of the face was better than in the marginal part. (4) Conclusions: In this study, the automatic determination of 3D facial anatomical landmarks was realized based on non-rigid registration algorithms. There is no significant difference in the automatic landmark localization accuracy between the TH-OCR algorithm and the MeshMonk algorithm, and both can meet the needs of oral clinical applications to a certain extent.

Principal polynomial shape analysis: A non-linear tool for statistical shape modeling

BACKGROUND AND OBJECTIVES

The most widespread statistical modeling technique is based on Principal Component Analysis (PCA). Although this approach has several appealing features, it remains hampered by its linearity. Principal Polynomial Analysis (PPA) can capture non-linearity in a sequential algorithm, while maintaining the interesting properties of PCA. PPA is, however, computationally expensive in handling shape surface data. To this end, we propose Principal Polynomial Shape Analysis (PPSA) as an adjusted approach for non-linear shape analyzes. The aim of this study was to assess PPSA's features, its model boundaries and its general applicability.

METHODS

PCA and PPSA-based shape models were investigated on one verification and three model evaluation experiments. In the verification experiment, the estimated mean of the PCA and PPSA model on a data set of synthetic lower limbs of different lengths in different poses were compared to the real mean. Further, the PCA-based and PPSA shape models were tested for three challenging cases namely for shape model creation of gait marker data, for regression analysis on aging faces and for modeling pose variation in full body scans. For the latter, additionally a Fundamental Coordinate Model (FCM) and a PPSA model on Fundamental Coordinate(FC) space was created. The performances were evaluated based on model-based accuracy, generalization, compactness and specificity.

RESULTS

In the verification experiment, the scaling error reduced from 75% to below 1% when employing PPSA instead of PCA for a training set with 180° angular variation. For the model evaluation experiments, the PPSA models described the data as accurate and generalized as the PCA-based shape models. The PPSA models were slightly more compact and specific (up to 30%) than the PCA-based models. In regression, PCA and PPSA-based parameterizations explained a similar amount of variation. Lastly, for the full body scans, applying PPSA to parameterizations improved the compactness and accuracy.

CONCLUSIONS

PPSA describes the non-linear relationships between principal variations in a parameterized space. Compared to standard PCA-based shape models, capturing the non-linearity reduced the nonsense information in the shape components and improved the description of the data mean.

Enhanced head-skull shape learning using statistical modeling and topological features

Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region. Thus, the objective of the present study was to enhance our head-to-skull prediction problem by using local topological features for training and predicting. Head and skull feature points were sampled on 329 head and skull models from computed tomography (CT) images. These feature points were classified into the back and facial topologies. Head-to-skull relations were trained using the partial least square regression (PLSR) models separately in the two topologies. A hyperparameter tuning process was also conducted for selecting optimal parameters for each training model. Thus, a new skull could be generated so that its shape was statistically fitted with the target head. Mean errors of the predicted skulls using the topology-based learning method were better than those using the non-topology-based learning method. After tenfold cross-validation, the mean error was enhanced 36.96% for the skull shapes and 14.17% for the skull models. Mean error in the facial skull region was especially improved with 4.98%. The mean errors were also improved 11.71% and 25.74% in the muscle attachment regions and the back skull regions respectively. Moreover, using the enhanced learning strategy, the errors (mean ± SD) for the best and worst prediction cases are from 1.1994 ± 1.1225 mm (median: 0.9036, coefficient of multiple determination (R²): 0.997274) to 3.6972 ± 2.4118 mm (median: 3.9089, R²: 0.999614) and from 2.0172 ± 2.0454 mm (median: 1.2999, R²: 0.995959) to 4.0227 ± 2.6098 mm (median: 3.9998, R²: 0.998577) for the predicted skull shapes and the predicted skull models respectively. This present study showed that more detailed information on the head-skull shape leads to a better accuracy level for the skull prediction from the head. In particular, local topological features on the back and face regions of interest should be considered toward a better learning strategy for the head-to-skull prediction problem. In perspective, this enhanced learning strategy was used to update our developed clinical decision support system for facial disorders. Furthermore, a new class of learning methods, called geometric deep learning will be studied.

Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study

BACKGROUND AND OBJECTIVES

Craniometric landmarks are essential in many biomedical applications, such as morphometric analysis or forensic identification. The process of locating landmarks is usually a manual and slow task, highly influenced by fatigue, skills and the experience of the practitioner. Localization errors are propagated and magnified in subsequent steps, which can result in incorrect measurements or assumptions. Thereby, standardization, reliability and reproducibility lay the foundations for the necessary accuracy in subsequent measurements or anatomical analysis. In this paper, we present an automatic method to annotate 3D surface skull models taking into account anatomical and geometrical features.

METHODS

The proposed method follows a hybrid structure where a deformable template is used to initialize the landmark positions. Then, a refinement stage is applied using prior anatomical knowledge to ensure a correct placement. Our proposal is validated over thirty 3D skull scans of male Caucasians, acquired by hand-held surface scanning, and a set of 58 craniometric landmarks. A statistical analysis was carried out to analyze the inter- and intra-observer variability of manual annotations and the automatic results, along with a visual assessment of the final results.

RESULTS

Inter-observer errors show significant differences, which are reflected in the expert consensus used as reference. The average localization error was 2.19±1.5 mm when comparing the automatic landmarks to the reference location. The subsequent visual analysis confirmed the reliability of the refinement method for most landmarks.

CONCLUSIONS

Repeated manual annotations show a high variability depending on both skills and expertise of the observer, and landmarks' location and characteristics. In contrast, the automatic method provides an accurate, robust and reproducible alternative to the tedious and error-prone task of manual landmarking.