Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.

ABSTRACT:

Background

Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning.

Purpose

In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images.

Methods

The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation.

SUBMITTER: Lei Y 

PROVIDER: S-EPMC9868056 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature