Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study

ABSTRACT:

Introduction

For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema).

Methods

This study presents the application of U-Net deep learning models that have been uniquely developed with region-specifi

SUBMITTER: DeSilvio T 

PROVIDER: S-EPMC10213753 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature