Automatic landmark identification in cone-beam computed tomography.

ABSTRACT:

Objective

To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.

Materials and methods

One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the

SUBMITTER: Gillot M 

PROVIDER: S-EPMC10440369 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature