Skip to main content

ORIGINAL RESEARCH article

Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1528654

Multi-Scale Channel Attention U-Net: A Novel Framework for Automated Gallbladder Segmentation in Medical Imaging

Provisionally accepted
Yiming Zhou Yiming Zhou 1Wen Xiaobo Wen Xiaobo 2Kang Fu Kang Fu 1Meina Li Meina Li 3Lin Sun Lin Sun 1Xiao Hu Xiao Hu 1*
  • 1 The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
  • 2 Qingdao Cancer Institute, Qingdao University, Qingdao, China
  • 3 Qingdao Municipal Hospital, Qingdao, Shandong Province, China

The final, formatted version of the article will be published soon.

    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.We retrospectively collected the CT images from 120 patients with liver cancer, based on which ground truth were 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), Hausdorff Distance (HD), Relative Volume Difference (RVD), and Volumetric Overlap Error (VOE) metrics.On the test set, MCAU-Net achieved DSC, JSC, PPV, SE, HD, RVD, and VOE values of 0.85±0.22, 0.79±0.23, 0.92±0.14, 0.84±0.23, 2.75±0.98, 0.18±0.48, and 0.22±0.42, respectively. Compared to the control models, U-Net, SEU-Net and TransUNet, the MCAU-Net improved DSC 0.06, 0.04 and 0.06, JSC by 0.09, 0.06 and 0.09, PPV by 0.08, 0.08 and 0.05, SE by 0.05 ,0.05 and 0.07, and reduced HD by 0.45, 0.28 and 0.41，RVD by 0.07, 0.03 and 0.07, VOE by 0.04, 0.02 and 0.08 respectively. Qualitative results revealed that MCAU-Net produced smoother and more accurate boundaries, closer to the expert delineation, with less over-segmentation and under-segmentation and improved robustness.The MCAU-Net model significantly improves gallbladder segmentation on CT images. It satisfies clinical requirements and enhances the efficiency of physicians, particularly in segmenting complex anatomical structures.

    Keywords: deep learning, U-net, Gallbladder, automatically delineated, multi-scale channel attention

    Received: 15 Nov 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Zhou, Xiaobo, Fu, Li, Sun and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xiao Hu, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.