Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

ABSTRACT:

Background and purpose

Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic.

Materials and methods

We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performanc

SUBMITTER: Rodriguez Outeiral R 

PROVIDER: S-EPMC8295848 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature