Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.

ABSTRACT:

Background and purpose

Spine delineation is essential for high quality radiotherapy treatment planning of spinal metastases. However, manual delineation is time-consuming and prone to interobserver variability. Automatic spine delineation, especially using deep learning, has shown promising results in healthy subjects. We aimed to evaluate the clinical utility of deep learning-based vertebral body delineations for radiotherapy planning purposes.

Materials and methods

A multi-scale convolutional neural network (CNN) was used for automatic segmentation and labeling. Two approaches were tested: the combined approach using one CNN for both segmentation and labeling, and the sequential approach using separate CNN's for these tasks. Training and internal validation data included 5

SUBMITTER: Arends SRS 

PROVIDER: S-EPMC8857663 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature