Patch-based convolutional neural networks for automatic landmark detection of 3D facial images in clinical settings.

ABSTRACT:

Background

The facial landmark annotation of 3D facial images is crucial in clinical orthodontics and orthognathic surgeries for accurate diagnosis and treatment planning. While manual landmarking has traditionally been the gold standard, it is labour-intensive and prone to variability.

Objective

This study presents a framework for automated landmark detection in 3D facial images within a clinical context, using convolutional neural networks (CNNs), and it assesses its accuracy in comparison to that of ground-truth data.

Material and methods

Initially, an in-house dataset of 408 3D facial images, each annotated with 37 landmarks by an expert, was constructed. Subsequently, a 2.5D patch-based CNN architecture was trained using this dataset to detect the same set of lan

SUBMITTER: Al-Baker B 

PROVIDER: S-EPMC11602742 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature