Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging.

ABSTRACT:

Background and purpose

Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations.

Materials and methods

T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear

SUBMITTER: Knuth F 

PROVIDER: S-EPMC9114680 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature