Automatic point detection on cephalograms using convolutional neural networks: A two-step method

Abstract

This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to compute various angles essential for diagnosis. Utilizing a custom-built desktop computer with a moderately priced graphics processing unit, cephalogram images were resized to 800×800 pixels. Training data comprised 833 images, augmented 100 times; an additional 179 images were used for testing. Due to the complexity of training with full-size images, training was divided into two steps. The first step reduced images to 128×128 pixels, recognizing all 18 points. In the second step, 100×100 pixels blocks were extracted from original images for individual point training. The program then measured six angles, achieving an average error of 3.1 pixels for the 18 points, with SNA and SNB angles showing an average difference of less than 1°.