Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation

Shi, Tianyi; Ding, Xiaohuan; Zhou, Wei; Pan, Feng; Yan, Zengqiang; Bai, Xiang; Yang, Xin

doi:10.1109/jbhi.2023.3274789

Cited by 8 publications

(1 citation statement)

References 45 publications

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“…The problem of vascular segmentation in medical imaging, which is essential for many applications, was taken on by the researchers in [36]. They presented the affinity feature strengthening network (AFN), a unique technique that uses a multiscale affinity approach that is insensitive to contrast to model geometry and modify pixel-wise segmentation features together.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

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The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

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Depth disentanglement strategy of latent space for medical image segmentation

Wang,

2024

Biomedical Signal Processing and Control

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PEA-Net: A progressive edge information aggregation network for vessel segmentation

Chen,

Fan,

Ding

et al. 2024

Computers in Biology and Medicine

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Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation

Cited by 8 publications

References 45 publications

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Depth disentanglement strategy of latent space for medical image segmentation

PEA-Net: A progressive edge information aggregation network for vessel segmentation

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