arXiv Computer Vision September 14, 2026 By Yang Liu, Yibing Shen, Keming Zhao, Cenk Jiang, Zhenghang Qian, Zhicheng Du, Chen Xiong, Qidong Shao, Zijun Lin, Yunqi Hu, Jingjing Zhou, Lian Zhang, Peter E. Lobie, Peiwu Qin, Chengming Yang

An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation

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arXiv Computer Vision

Sep 22

U-PEN Mamba: Progressive Expansion with Selective State-Space Modeling for Efficient Retinal Vessel Segmentation

arXiv:2609.24049v1 Announce Type: new Abstract: Accurate retinal vessel segmentation is important for computer-aided ophthalmic analysis, yet thin vessels, low contrast, and severe foreground-backgro...

By Abel A. Reyes-Angulo, Sidike Paheding, Vijayan K. Asari, Mohammad Alam, Jeevan Devagiri

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arXiv Machine Learning

Jul 7

Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy

arXiv:2607. 03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools.

By Rashadul Hasan Badhon, Atalie Carina Thompson, Jennifer I. Lim, Theodore Leng, Minhaj Nur Alam

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arXiv AI

Jun 16

Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans

arXiv:2606. 16234v1 Announce Type: cross Abstract: Fundus fluorescein angiography (FFA) is critical for assessing retinal vascular abnormalities, but its acquisition is invasive and not always feasible.

By Tengfei Ma, Ruiqi Wu, Chenran Zhang, Ye Geng, Na Su, Xiangyuan Duanmu, Tao Zhou, Yi Zhou, Wen Fan

multimodal benchmarks safety

arXiv Computer Vision

Sep 1

retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

arXiv:2602.08580v4 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculat...

By Jose D. Vargas Quiros, Michael J. Beyeler, Sofia Ortin Vela, EyeNED Reading Center, Sven Bergmann, Caroline C. W. Klaver, Bart Liefers, VascX Research Consortium

computer-vision

arXiv Computer Vision

Aug 26

Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features

arXiv:2608.24723v1 Announce Type: new Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent re...

By Xiaoyan Li, Shixin Xu, Arvind Gupta, Huaxiong Huang

llms computer-vision safety

arXiv Machine Learning

Jul 28

Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures

arXiv:2406. 13128v2 Announce Type: replace-cross Abstract: Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability in extracted morphological properties.

By Jo\~ao Pedro Parella, Matheus Viana da Silva, Cesar Henrique Comin

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Related stories

U-PEN Mamba: Progressive Expansion with Selective State-Space Modeling for Efficient Retinal Vessel Segmentation

Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy

Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans

retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features

Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures