Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review

Richu Jin, Yongning Cai, Shiyang Zhang, Ting Yang, Haibo Feng, Hongyang Jiang, Xiaoqing Zhang, Yan Hu, Jiang Liu

Research output: Journal PublicationReview articlepeer-review

5 Citations (Scopus)

Abstract

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.

Original languageEnglish
Article number1191999
JournalFrontiers in Neuroscience
Volume17
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • artificial intelligence
  • fiber tracking
  • image segmentation
  • medical image analysis
  • optic nerve fiber
  • visual pathway

ASJC Scopus subject areas

  • General Neuroscience

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