Dive into Plane: Lightweight &amp; Modular Linear Projection Cross-dimensional Network for Retinal Vessel Segmentation in OCTA Images

Zhong, Yingying; Xu, Mengqiu; Wu, Ming

doi:10.1109/bibm55620.2022.9995294

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…However, given the vast variation in segmentation algorithms and their target ROIs, the tabulated results lack uniformity (Table 12). For instance, some techniques delineate only the retinal vasculature [36][37][38], while the others segment both retinal vessels and FAZ [19,[39][40][41]. Very few methods detect the arteries, veins, capillaries, and FAZ separately [2,20].…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…The segmentation metrics used also vary between different approaches. The Dice ratio and IoU score are the commonly computed metrics [2,19,20,37,41], while accuracy, sensitivity, and specificity are obtained by a few papers [36,38,40]. Given this wide variability in the representation of results on the same dataset, especially with reference to the regions segmented, it is hard to perform a one-to-one comparison of results.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

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DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

Raja,

Gopi,

Francis

et al. 2024

Preprint

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The study and analysis of retinal blood vessels and foveal avascular zone (FAZ) are clinically significant as they help in the early diagnosis of several diseases like diabetic retinopathy, glaucoma, neovasculariza-tion, artery or vein occlusion, and age-related macular degeneration. Recently, optical coherence tomography angiography (OCTA) imaging has been used to visualize the retina due to its non-invasive nature and fast imaging. However, manual retinal vasculature and FAZ segmentation are tedious and time-consuming. In this context, we propose an automated deep-learning-based segmentation method. The traditional U-Net is modified with a pre-trained EfficientNet-B0 in the encoder and a combined spatial, channel attention, squeeze, and excitation mechanism in the decoder. The proposed algorithm is validated on the publicly available OCTA-500 database. Mean Dice coefficients of 88.38, 88.40, 88.48, and 94.55 are obtained for capillary, artery, vein, and FAZ segmenta-tion in the OCTA 6 mm dataset. Whereas mean Dice scores of 90.48, 90.70, 89.59, and 97.98 are reported in the OCTA 3 mm subset for capillary , artery, vein, and FAZ segmentation, respectively. This segmentation performance is superior to other recent methods in the state-of-the-art literature. Overall, the contributions of our work are summarized as DRL-ASOCT follows. The proposed architecture is simple, easy to implement, and requires less memory and time than other networks. Further, we separately segment the retinal vessels (arteries, veins, and capillaries) and achieve improved segmentation accuracy relative to other approaches.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

Raja,

Gopi,

Francis

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Pissas et al (2020) the architecture of IPN-V2 by introducing the Quadruple Attention module to capture cross-dimensional dependencies and proposes a feature fusion module for volumetric data reuse. Zhong et al (2022) proposes a lightweight linear projection module for dimensionality reduction and spatial feature transformation. RPS-Net introduces two parallel networks to learn global and local features, and also uses 2D projection maps as additional inputs.…”

Section: Introductionmentioning

confidence: 99%

LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images

Yang,

Li,

Xiao

et al. 2024

Phys. Med. Biol.

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Objective. Retinal vessel segmentation from optical coherence tomography angiography (OCTA) volumes is significant in analyzing blood supply structures and the diagnosing ophthalmic diseases. However, accurate retinal vessel segmentation in 3D OCTA remains challenging due to the interference of choroidal blood flow signals and the variations in retinal vessel structure. Approach. This paper proposes a layer attention network (LA-Net) for 3D-to-2D retinal vessel segmentation. The network comprises a 3D projection path and a 2D segmentation path. The key component in the 3D path is the proposed multi-scale layer attention module, which effectively learns the layer features of OCT and OCTA to attend to the retinal vessel layer while suppressing the choroidal vessel layer. This module also efficiently captures 3D multi-scale information for improved semantic understanding during projection. In the 2D path, a reverse boundary attention module is introduced to explore and preserve boundary and shape features of retinal vessels by focusing on non-salient regions in deep features. Main results. Experimental results in two subsets of the OCTA-500 dataset showed that our method achieves advanced segmentation performance with Dice similarity coefficients of 93.04% and 89.74%, respectively. Significance. The proposed network provides reliable 3D-to-2D segmentation of retinal vessels, with potential for application in various segmentation tasks that involve projecting the input image. The code for LA-Net is available at https://github.com/y8421036/LA-Net.

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Dive into Plane: Lightweight & Modular Linear Projection Cross-dimensional Network for Retinal Vessel Segmentation in OCTA Images

Cited by 2 publications

References 15 publications

DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images

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