Cross-modality cerebrovascular segmentation based on pseudo-label generation via paired data.

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  • Author(s): Guo Z;Guo Z; Feng J; Feng J; Lu W; Lu W; Yin Y; Yin Y; Yang G; Yang G; Zhou J; Zhou J
  • Source:
    Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2024 Jul; Vol. 115, pp. 102393. Date of Electronic Publication: 2024 May 01.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Elsevier Science Country of Publication: United States NLM ID: 8806104 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0771 (Electronic) Linking ISSN: 08956111 NLM ISO Abbreviation: Comput Med Imaging Graph Subsets: MEDLINE
    • Publication Information:
      Publication: Tarrytown Ny : Elsevier Science
      Original Publication: New York : Pergamon Press, c1988-
    • Subject Terms:
      Magnetic Resonance Angiography*/methods ; Angiography, Digital Subtraction*/methods ; Computed Tomography Angiography*/methods; Humans ; Neural Networks, Computer ; Cerebrovascular Disorders/diagnostic imaging ; Image Processing, Computer-Assisted/methods
    • Abstract:
      Accurate segmentation of cerebrovascular structures from Computed Tomography Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction Angiography (DSA) is crucial for clinical diagnosis of cranial vascular diseases. Recent advancements in deep Convolution Neural Network (CNN) have significantly improved the segmentation process. However, training segmentation networks for all modalities requires extensive data labeling for each modality, which is often expensive and time-consuming. To circumvent this limitation, we introduce an approach to train cross-modality cerebrovascular segmentation network based on paired data from source and target domains. Our approach involves training a universal vessel segmentation network with manually labeled source domain data, which automatically produces initial labels for target domain training images. We improve the initial labels of target domain training images by fusing paired images, which are then used to refine the target domain segmentation network. A series of experimental arrangements is presented to assess the efficacy of our method in various practical application scenarios. The experiments conducted on an MRA-CTA dataset and a DSA-CTA dataset demonstrate that the proposed method is effective for cross-modality cerebrovascular segmentation and achieves state-of-the-art performance.
      Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
      (Copyright © 2024 Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Cerebral vessel segmentation; Pseudo labels; Registration; Unsupervised domain adaptation
    • Publication Date:
      Date Created: 20240505 Date Completed: 20240601 Latest Revision: 20240601
    • Publication Date:
      20240602
    • Accession Number:
      10.1016/j.compmedimag.2024.102393
    • Accession Number:
      38704993