Adaptive boundary-enhanced Dice loss for image segmentation.

Journal: Biomedical signal processing and control
Published Date: Feb 20, 2025

Abstract

Deep learning is widely utilized for medical image segmentation, and its effectiveness is significantly influenced by the choice of specialized loss functions. In this study, we introduce an adaptive boundary-enhanced Dice (ABeDice) loss function, which integrates an exponential recursive complementary (ERC) function with the traditional Dice loss to improve segmentation accuracy. The ERC function leverages the prediction probability of each pixel and its complement to enhance the detection and localization of object boundaries. By dynamically adjusting the distribution of prediction probabilities, the ABeDice loss prioritizes higher probabilities, thereby improving both quantization potential and convergence rate. This adaption not only boosts the learning capability of the network but also enhances its segmentation performance. The effectiveness of the ABeDice loss was validated through extensive experiments using the Swin-Unet on three public datasets, including REFUGE, ISIC2018, and RIT-Eyes. The results showed that ABeDice achieved average Dice similarity coefficient of 0.9114, 0.8940, and 0.9418, respectively, outperforming traditional Dice loss and its variants, such as Generalized Dice loss, Tervkey loss, and Sensitivity-Specifity loss. The code is available at https://github.com/wmuLei/ABeDice.

Authors

  • Yanyan Zheng
    Department of Neurology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou 325000, China.
  • Bihan Tian
    National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
  • Shuchen Yu
    National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
  • Xiaoguo Yang
    Department of Neurology, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou 325041, China.
  • Qingxiang Yu
    Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China.
  • Jie Zhou
    Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
  • Gaoqiang Jiang
    National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
  • Qinxiang Zheng
    Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
  • Jiantao Pu
    Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals