Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

ABSTRACT:

Introduction

We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images.

Methods

A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional

SUBMITTER: Atici SF 

PROVIDER: S-EPMC9249196 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature