Online Bayesian Approximation based Uncertainty Aware Model for Ophthalmic Image Segmentation.

Journal: IEEE journal of biomedical and health informatics
Published Date:

Abstract

The robust segmentation of different targets in multiple modality images is challenging due to factors such as low contrast, variations in target size and shape, and interference from diseases, which may lead to segmentation ambiguity. In addition, the assessment of the reliability of artificial intelligence is crucial for its clinical application. This paper proposes the Online Bayesian approximation based Uncertainty-aware Network (OBU-Net) for robust ophthalmic image segmentation. Our approach introduces an efficient online Bayesian method to update a spatial uncertainty map during training continuously. Then, the Spatial Uncertainty Aware Block (SUA-B) leverages the uncertainty map to localize and prioritize attention to ambiguous regions. Additionally, we extract pixel-wise confidence from multi-scale predictions to integrate hierarchical predictions. We compare OBU-Net with state-of-the-art (SOTA) methods on six datasets. The experimental results demonstrate that our method achieves the best overall performance across different modalities and segmentation tasks, highlighting the robustness of our approach. Additionally, metamorphic testing experiments were conducted, exploring the algorithm's stability against random perturbations. Lastly, we propose an image-level uncertainty score and demonstrate its effectiveness for evaluating the model's segmentation reliability.

Authors

  • Yinglin Zhang
  • Risa Higashita
    Tomey Corporation, Nagoya, Japan; Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Lingxi Zeng
  • Jialin Li
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Ruiling Xi
  • Tianhang Liu
    Department of Computer Science and Engineering, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, 518055, Guangdong, China.
  • Huazhu Fu
    A*STAR, Singapore, Singapore.
  • Dave Towey
  • Ruibin Bai
    State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, PR China; Key Laboratory of Biology and Cultivation of Herb Medicine, Ministry of Agriculture and Rural Affairs, Beijing 100700, PR China; Evaluation and Research Center of Daodi Herbs of Jiangxi Province, Ganjiang New District 330000, PR China.
  • Jiang Liu
    Department of Pharmacy, The Fourth Hospital of Hebei Medical University Shijiazhuang 050000, Hebei, China.

Keywords

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