Abstract：
:The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Mookiah MRK,Hogg S,MacGillivray TJ,Prathiba V,Pradeepa R,Mohan V,Anjana RM,Doney AS,Palmer CNA,Trucco Edoi
10.1016/j.media.2020.101905subject
Has Abstractpub_date
2021-02-01 00:00:00pages
101905eissn
1361-8415issn
1361-8423pii
S1361-8415(20)30269-3journal_volume
68pub_type
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