TY - JOUR
T1 - Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images
T2 - a comprehensive review
AU - Jin, Richu
AU - Cai, Yongning
AU - Zhang, Shiyang
AU - Yang, Ting
AU - Feng, Haibo
AU - Jiang, Hongyang
AU - Zhang, Xiaoqing
AU - Hu, Yan
AU - Liu, Jiang
N1 - Publisher Copyright:
Copyright © 2023 Jin, Cai, Zhang, Yang, Feng, Jiang, Zhang, Hu and Liu.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - fiber tracking
KW - image segmentation
KW - medical image analysis
KW - optic nerve fiber
KW - visual pathway
UR - http://www.scopus.com/inward/record.url?scp=85161427109&partnerID=8YFLogxK
U2 - 10.3389/fnins.2023.1191999
DO - 10.3389/fnins.2023.1191999
M3 - Review article
AN - SCOPUS:85161427109
SN - 1662-4548
VL - 17
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 1191999
ER -