Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

ABSTRACT:

Objectives

To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI.

Methods

MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC).

Results

The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually

SUBMITTER: Lin YC 

PROVIDER: S-EPMC10415433 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature