Automated anatomical landmark detection on 3D facial images using U-NET-based deep learning algorithm.

ABSTRACT:

Background

Facial anthropometry based on 3-dimensional (3D) imaging technology, or 3D photogrammetry, has gained increasing popularity among surgeons. It outperforms direct measurement and 2-dimensional (2D) photogrammetry because of many advantages. However, a main limitation of 3D photogrammetry is the time-consuming process of manual landmark localization. To address this problem, this study developed a U-NET-based deep learning algorithm to enable automated and accurate anatomical landmark detection on 3D facial models.

Methods

The main structure of the algorithm stacked 2 U-NETs. In each U-NET block, we used 3×3 convolution kernel and rectified linear unit (ReLU) as activation function. A total of 200 3D images of healthy cases, acromegaly patients, and localized sclero

SUBMITTER: Chong Y 

PROVIDER: S-EPMC10963813 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature