Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.

ABSTRACT:

Background

Accurate segmentation of lung cancer gross tumor volume (GTV) on computed tomography (CT) is critical for radiotherapy planning yet remains difficult due to low tumor-tissue contrast, small target size, and high intratumoral heterogeneity. This study aimed to develop and validate an automatic method-a GTV segment-anything model generative adversarial network (GTV-SAMGAN)-for accurate, robust, and clinically efficient GTV segmentation on CT, with particular emphasis on small, low-contrast, and heterogeneous lesions.

Methods

We propose GTV-SAMGAN, built upon SAM medical 2D image (SAM-Med2D), integrating a newly developed GTV-minimal feature integration technology MFIT (GTV-MFIT) module with a GAN-based training scheme. The performance of GTV-SAMGAN was evaluated on

SUBMITTER: Yi C 

PROVIDER: S-EPMC12780636 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature