Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network

Abstract

In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient's satisfaction. The penetrating form of high-energy electromagnetic radiation is highly impacted on human health, which has often raised concerns of alternative method for facial analysis. The critical stage to assess nasal morphology is the nasal analysis on its anthropology that is highly reliant on the understanding of the structural features of the nasal radix. For example, the shape and size of nasal bone features, skin thickness, and also body factors aggregated from different facial anthropology values. In medical diagnosis, however, the morphology of the nasal bone is determined manually and significantly relies on the clinician's expertise. Furthermore, the evaluation anthropological keypoint of the nasal bone is nonrepeatable and laborious, also finding widely differ and intralaboratory variability in the results because of facial soft tissue and equipment defects. In order to overcome these problems, we propose specialized convolutional neural network (CNN) architecture to accurately predict nasal measurement based on digital 2D photogrammetry. To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. Through its result, the back-propagation neural network (BPNN) indicated the correlation between differences in human body factors mentioned are height, weight known as body mass index (BMI), age, gender, and the nasal bone dimension of the participant. With full of parameters could the nasal morphology be diagnostic continuously. The model's performance is evaluated on various newest architecture models such as DenseNet, ConvNet, Inception, VGG, and MobileNet. Experiments were directly conducted on different facials. The results show the proposed architecture worked well in terms of nasal properties achieved which utilize four statistical criteria named mean average precision (mAP), mean absolute error (MAE), R-square (R ²), and T-test analyzed. Data has also shown that the nasal shape of Southeast Asians, especially Vietnamese, could be divided into different types in two perspective views. From cadavers for bony datasets, nasal bones can be classified into 2 morphological types in the lateral view which "V" shape was presented by 78.8% and the remains were "S" shape evaluated based on Lazovic (2015). With 2 angular dimension averages are 136.41 ± 7.99 and 104.25 ± 5.95 represented by the nasofrontal angle (g-n-prn) and the nasomental angle (n-prn-sn), respectively. For frontal view, classified by Hwang, Tae-Sun, et al. (2005), nasal morphology of Vietnamese participants could be divided into three types: type A was present in 57.6% and type B was present in 30.3% of the noses. In particular, types C, D, and E were not a common form of Vietnamese which includes the remaining number of participants. In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. Nasal analysis can replace MRI imaging diagnostics that are reflected by the risk to human body.

Figure 1

Anthropometric landmarks have been indirectly measured in three perspective views; all dimensions are in millimeters (mm) based on split length on the ruler belongs with an image. Image modified with permission. tr: trichion; g: glabella, s: sellion; r: rhinion; en: endocanthion; ex: exocanthion; mf: maxillofrontale; prm: pronasale; al: alare; ac: subalare; sn: subnasale; zy: zygion; li: labrale inferius; ls: labrale superius; ch: cheilion; pg: pogonion; gn: gnathion.

Figure 2

The framework of proposals in nasal bone shape prediction.

Figure 3

Angular measurements based on indirect anthropology on the patient's skin with angular measurement to be listed as (a) g-n-prn, n-prn-pg, g-sn-pg, n-prn-sn and (b) n-prn-sn, cm-sn-ls, li-sm-pg, g-sn-pg. Image used with permission of subject.

Figure 4

Distances are practically measured for the nasal bones, in which horizontal d4, d5, and d3 are the width of nasal bones at the nasomaxillary suture line, nasion, and nasofrontal suture, perspectively. With red line, it illustrated the distance between nason and sellion (dimensions are shown in mm) (a). Nasal bone had been removed, ligament and soft tissue captured from the right side (b).

Figure 5

Proposed convolutional neural network (CNN) model for anthropological localization.

Figure 6

Proposed BPNN model for nasal bone morphology prediction.

Figure 7

Features extracted in output layer and representation of feature map in every CNN's layer.

Figure 8

Accuracy and lost performed of detecting keypoint by 5 different network models over a certain period.

Figure 9

Anthropometric keypoints have been predicted by CNNs model in three perspective views; (a, c) frontal, lateral, and basal views, respectively. All dimension units are in millimeter (mm) based on split length on the ruler belongs with an image can be automatically measured.

Figure 10

Nasal morphology was presented in the lateral image with back-propagation, including the bounding on nose shape and approximately size of nasal bone.