Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net

Abstract

The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 k × 3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 ± 2.17 mm. The model with the cascade CNN showed an average error of 0.79 ± 0.91 mm (paired t-test, p = 0.0015). The orthodontist's average error of reproducibility was 0.80 ± 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.

Figure 1

An example case of forty-two landmarks (numbered 0–41) in a cephalometric X-ray lateral image of size 2 k × 3 k pixel used in this study.

Figure 2

The general schematic of our proposed algorithm for finding the exact location of landmarks with a cascade network. The proposed algorithm consists of two parts, ROI detection (upper part) to propose the area of interest and the landmark prediction (lower part) to find the exact location of landmarks.

Figure 3

Two sizes of ROIs in the cephalometric X-ray. (a) ROIs with 256 × 256 and 512 × 512 size were extracted by landmarks. (b) Sella, nasion, and menton requiring a small ROI with 256 × 256 size (red), and (c) hinge, corpus and Md6 root requiring a wide ROI with 512 × 512 size (blue).

Figure 4

Regions of interests (ROIs) detection and landmark prediction results with different sizes depending on the information of each landmark. (a) Predicted ROIs (red and blue boxes) by RetinaNet algorithm, (b) ROI patches used for input of semantic segmentation for predicting a landmark, and (c) ground truth masks from the test set of each landmark.

Figure 5

Experimental results of our proposed model. (a) shows the highest accuracy, and (b) shows the lowest accuracy in cephalometric X-ray images (red point, predicted landmark by deep learning; Green point, the ground truth).