Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.

ABSTRACT:

Background

Accurate landmark localization is important for three-dimensional (3D) cephalometric analysis. Although deep learning has shown promising performance for 3D landmark localization, the high computational burden of processing volumetric data remains a challenge. The 2.5D networks have emerged to provide the good performance while mitigating computational and memory requirements in the medical domain. Therefore, we compared the performance of 2D, 2.5D and 3D network-based landmark localization.

Methods

We collected landmark datasets from the volumetric computed tomography (CT) scans of 40 patients. We implemented the 2D, 2.5D and 3D networks for 3D landmark localization. Additionally, we designed a global-to-local loss to mitigate foreground-background imbalance, and

SUBMITTER: Kim SM 

PROVIDER: S-EPMC12659582 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature