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<p>Medical image analysis plays a pivotal role in diagnosis and treatment. However, the diverse characteristics of various imaging modalities often demand distinct processing approaches. In this study, we introduce UMSSNet, a versatile method for medical image segmentation that leverages data from heterogeneous medical images across different scales. By utilizing Gaussian pyramid-based image processing techniques, we transform the heterogeneous medical images into a uniform multi-scale image structure. Subsequently, UMSSNet integrates multi-scale image features, encompassing contextual information, and adopts a dynamic and hierarchical approach to process images at various scales, emulating the decision-making process of human pathologists and facilitating precise image segmentation. We tested UMSSNet on publicly available datasets consisting of various forms of medical images, including WSI, Biopsy slides, CT, MRI, X-ray, Colonoscopy, Fundus, and CMR, as well as private datasets of Immunohistochemical staining, Immunofluorescence staining, and Masson staining sample images. UMSSNet demonstrated performance comparable to state-of-the-art medical image segmentation methods Furthermore, the generalizability of UMSSNet in segmenting heterogeneous medical images holds promise for future research in the analysis of multimodal medical data.</p>

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UMSSNet: a unified multi-scale segmentation network for heterogeneous medical images

  • 期刊论文

  • 发表日期：

    2025年1月25日

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  • Zerui Xu,
  • Dechao Chen,
  • Wenyan Gong

摘要

Medical image analysis plays a pivotal role in diagnosis and treatment. However, the diverse characteristics of various imaging modalities often demand distinct processing approaches. In this study, we introduce UMSSNet, a versatile method for medical image segmentation that leverages data from heterogeneous medical images across different scales. By utilizing Gaussian pyramid-based image processing techniques, we transform the heterogeneous medical images into a uniform multi-scale image structure. Subsequently, UMSSNet integrates multi-scale image features, encompassing contextual information, and adopts a dynamic and hierarchical approach to process images at various scales, emulating the decision-making process of human pathologists and facilitating precise image segmentation. We tested UMSSNet on publicly available datasets consisting of various forms of medical images, including WSI, Biopsy slides, CT, MRI, X-ray, Colonoscopy, Fundus, and CMR, as well as private datasets of Immunohistochemical staining, Immunofluorescence staining, and Masson staining sample images. UMSSNet demonstrated performance comparable to state-of-the-art medical image segmentation methods Furthermore, the generalizability of UMSSNet in segmenting heterogeneous medical images holds promise for future research in the analysis of multimodal medical data.