Joint-Seg: Treat Foveal Avascular Zone and Retinal Vessel Segmentation in OCTA Images as a Joint Task

Hu, Kai; Jiang, Shuai; Zhang, Yuan; Li, Xuanya; Gao, Xieping

doi:10.1109/tim.2022.3193188

Cited by 15 publications

(5 citation statements)

References 55 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

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“…The benefit of open-source datasets can be observed in other medical modalities. The creation of the ROSE dataset [192] and the OCTA-500 [193,194] have pushed higher the number of DLbased OCTA vessel segmentation papers, with many studies published using only open source datasets [195][196][197]. Additionally, as mentioned, an ML contest could be created featuring such datasets.…”

Section: Conclusion Challenges and Opportunitiesmentioning

confidence: 99%

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Gouzou,

Taimori,

Haloubi

et al. 2023

Methods Appl. Fluoresc.

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Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks.Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources.In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics.However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm.In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

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“…The significance of identifying biomarkers from optical coherence tomography angiography (OCTA) images-more especially, the foveal avascular zone (FAZ) and retinal veins (RV)-was discussed by the researchers in [25]. They suggested Joint-Seg, a joint segmentation framework that facilitated the simultaneous extraction of FAZ and RV from enface OCTA images.…”

Section: Review Of Existing Modelsmentioning

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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Joint-Seg: Treat Foveal Avascular Zone and Retinal Vessel Segmentation in OCTA Images as a Joint Task

Cited by 15 publications

References 55 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

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

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

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