Automated vessel diameter quantification and vessel tracing for <scp>OCT</scp> angiography

Wei, Wei; Zhang, Qinqin; Rayner, Samuel G.; Wan, Qin; Cheng, Yuxuan; Wang, Fupeng; Zheng, Ying; Wang, Ke

doi:10.1002/jbio.202000248

Cited by 8 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12. A possible other macula disorders were also noticed in the RVO and CSC cases (subjects # [17][18][19][20]. For all subjects, the ophthalmologists claimed that the methodology could help determine the disease's precise axial and transversal location and 3D size.…”

Section: Discussionmentioning

confidence: 97%

“…There are other OCTA metrics that were reported in the literature, such as vessel or perfusion density, vessel area density, and skeletonized vessel density or vessel length index [19,20,[41][42][43][44][45]. They were evaluated to assess the integrity (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…These methods cover automatic or semi-automatic segmentation algorithms designed on both the macro and micro macula's vasculature levels. Some researchers suggested extending the OCTA segmentation tasks by producing quantification parameters [17,19,20,[41][42][43][44][45], such as OCTA vessels' length, area density, and diameters. Some studies moved beyond the segmentation and quantitative analysis toward exploring the use of Artificial Intelligence (AI) techniques such as Deep Learning [21-23, 46, 48] and Machine Learning [24,47] to classify the delineated vasculature regions into normal or abnormal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstruction and Visualization of 5μm Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

et al. 2023

View full text Add to dashboard Cite

A Computerized Medical Image Processing (CMIP) method is proposed to address the current challenges of optical coherence tomography angiography (OCTA): (1) the need for observing the macula vasculature concerning natural curvature of the macula region; (2) the need for generating OCTA frames at successive small depths in all macula layers; and (3) the need for enhancing the visibility of blood vessels, particularly below the outer retina region. The proposed CMIP method involves image preprocessing, reconstruction, and enhancement stages. Twenty subjects were obtained from the OCTA500 dataset, which was obtained from the OptoVue OCTA machine. The 20 subjects comprise the two OCTA fields of view (FOV), right and left eyes (OD and OS), and five common macula disorders. The sequential enface OCTA images at 5µm macula depths were displayed. The presentation of the macula vasculature was enhanced at all depths. The resulting new ophthalmic views enable: (1) avoiding the superimposition of macula vasculature into a projection map; (2) enhancing the OCTA presentation of blood vessels; and (3) inspecting the macula's 3D oval-shaped. The proposed CMIP method can generate sectional macula coronal views (MCV) for every 5µm depth, clarifying the macula's curvature in a way that has not been presented in OCTA machines. Therefore, "tracking" the 3D propagation of the blood vessel network at all macula depths becomes possible. Furthermore, the blood vessels' display at all macula depths, including the deep choroid layers, is enhanced. The method yields futuristic ophthalmic advantages that would allow the physician to precisely inspect the 3D localization and diffusion of the macula disorders. The method is invariant to the OCTA's FOVs, macula disorder, and OD or OS eye.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction and Visualization of 5μm Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

et al. 2023

View full text Add to dashboard Cite

show abstract

“…There have been many manual, semi-automatic and automatic methodologies proposed to delineate and trace elongated structures some of which are based on algorithms that can be considered more general as they can be applied in a context other than tracing, for instance, skeletonisation [14][15][16], watershed transform [17,18], medial axis transform [19][20][21], Scale Space [22,23] and even Edge Detection [24]. However, many of these methodologies have been proposed for tracing in specific applications, which may imply that these have been fine-tuned to the characteristics of the datasets being analysed, e.g., neuron tracing [25][26][27], retinal images [28][29][30], angiography [31][32][33], intravital vasculature observed with confocal, fluorescence or light microscopy [34][35][36], and extracellular matrix [37,38]. In addition, deep learning architectures like the well-known U-Net [39] have been widely used in image analysis and provided excellent results without the need of hand-crafted features or algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Arafat,

Cuesta-Apausa,

Castellano

et al. 2024

Preprint

View full text Add to dashboard Cite

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one new approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with very different characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace_Ridges.

show abstract

“…It facilitates direct visualization of the retinal microvasculature in a safe and quick manner. Intensity-based optical microangiography (OMAG) algorithms developed by Wang et al can be applied to SD-OCTA in order to quantitatively analyze retinal vessel morphology in individuals with normal and abnormal retinal microvascular functions [11‒13]. The present study investigated the retinal microvascular characteristics by SD-OCTA technique in prediabetic individuals and compared the features with nondiabetic controls and DM patients.…”

Section: Introductionmentioning

confidence: 99%

Early Retinal Microvasculopathy in Prediabetic Patients and Correlated Factors

Zhu

Wang

et al. 2022

Ophthalmic Res

Self Cite

View full text Add to dashboard Cite

Introduction: We aimed to detect early retinal microcirculation changes in prediabetic patients and investigate their correlation with clinical examinations. Methods: Forty-seven prediabetic individuals, 29 controls, and 81 type 2 diabetic mellitus (T2DM) patients were enrolled in this study. A review of clinical data and spectral-domain optical coherence tomography angiography (SD-OCTA) parameters of macular vessel diameter (VD), foveal avascular zone (FAZ), and macular vessel area density (VAD) was performed. Results: Levels of low-density lipoprotein cholesterol and triglycerides in prediabetes and T2DM groups were significantly higher than those in the control group. Urine microalbumin-to-creatinine ratio (ACR) was mildly and moderately increased in the prediabetes and T2DM groups, respectively. Estimated glomerular filtration rate of the three groups were within the normal range. SD-OCTA showed that VAD in the superficial macular area was decreased in the prediabetes group compared to the control group (p=0.01). The FAZ size, particularly in the deep layer, was expanded in the prediabetes group. In the deep retinal layer of the macular area, VD and FAZ size in the prediabetes group were larger than those in the control group. In the prediabetes group, the axial length was significantly correlated with macular VD and FAZ size (p<0.05), and ACR was correlated with FAZ size (p<0.05). Age had a negative correlation with VAD (p<0.01). ACR had a positive correlation with FAZ size (p<0.05). Conclusions: Enlargement and irregularity of the FAZ area, deep capillary dilation, and decrease in VAD occur in the retina of prediabetes patients with mild kidney function impairment.

show abstract

Automated vessel diameter quantification and vessel tracing for OCT angiography

Abstract: Optical coherence tomography angiography (OCTA) is capable of noninvasively imaging the vascular networks within circulatory tissue

Cited by 8 publications

References 40 publications

Reconstruction and Visualization of 5μm Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

Reconstruction and Visualization of 5μm Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Early Retinal Microvasculopathy in Prediabetic Patients and Correlated Factors

Contact Info

Product

Resources

About