Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images

Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images

This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP late...

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Bibliographic Details
Main Authors: Min-Jung Kim, Yi Liu, Song Hee Oh, Hyo-Won Ahn, Seong-Hun Kim, Gerald Nelson
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Sensors
Subjects:
artificial intelligence
convolutional neural networks
automatic identification
lateral cephalograms
cone-beam computed tomography
maximum intensity projection (MIP)
Online Access:https://www.mdpi.com/1424-8220/21/2/505

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