Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning.

ABSTRACT:

Background

The aim of this study was to establish a correlation model between external surface motion and internal diaphragm apex movement using machine learning and to realize online automatic prediction of the diaphragm motion trajectory based on optical surface monitoring.

Methods

The optical body surface parameters and kilovoltage (kV) X-ray fluoroscopic images of 7 liver tumor patients were captured synchronously for 50 seconds. The location of the diaphragm apex was manually delineated by a radiation oncologist and automatically detected with a convolutional network model in fluoroscopic images. The correlation model between the body surface parameters and the diaphragm apex of each patient was developed through linear regression (LR) based on synchronous datasets befo

SUBMITTER: Dai Z 

PROVIDER: S-EPMC10102745 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature