Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging.

ABSTRACT:

Objectives

To develop a novel automatic delineation model, the Multi-Scale Channel Attention U-Net (MCAU-Net) model, for gallbladder segmentation on CT images of patients with liver cancer.

Methods

We retrospectively collected the CT images from 120 patients with liver cancer, based on which ground truth was manually delineated by physicians. The images and ground truth constitute a dataset, which was proportionally divided into a training set (54%), a validation set (6%), and a test set (40%). Data augmentation was performed on the training set. Our proposed MCAU-Net model was employed for gallbladder segmentation and its performance was evaluated using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), Positive Predictive Value (PPV), Sensitivity (SE)

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11810919 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature