Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling.

ABSTRACT:

Background and purpose

Computed tomography (CT) radiomics of head and neck cancer (HNC) images is susceptible to dental implant artifacts. This work devised and validated an automated algorithm to detect CT metal artifacts and investigate their impact on subsequent radiomics analyses. A new method based on features from total variation, gradient directional distribution, and Hough transform was developed and evaluated.

Materials and methods

Two HNC datasets were analyzed: a training set of 131 patients for developing the detection algorithm and a testing set of 220 patients. Seven designated features were extracted from ROIs (regions of interest) and machine learning with random forests was used for building the artifact detection algorithm. Performance was assessed using th

SUBMITTER: Wei L 

PROVIDER: S-EPMC7807651 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature