Construction of retinal vascular trees via curvature orientation prior

Lyu, Xingzheng; Yang, Qifan; Xia, Shunren; Zhang, Sanyuan

doi:10.1109/bibm.2016.7822548

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Since we used the proposed SEGAN model [22] trained by six public dataset containing fundus images and only tested the model on our dataset, there were some discontinuities in the segmented blood vessels resulting from the proposed SEGAN model. To solve this problem, we used a post-processing step and took advantage of two useful algorithms, a region growing method and a missing algorithm [24], to fill discontinuous parts of the segmented blood vessels.…”

Section: Post Processingmentioning

confidence: 99%

“…Algorithm 1 states our proposed region growing approach in detail in which pi represent ith terminal point, S1 is a set of neighboring points satisfying a certain criterion that was explained above and finally PS1,i represent ith point in S1. In the next step, we used the missing algorithm proposed [24] to compensate for some small discontinuities left over from the first step. The proposed missing algorithm corrected the small disconnected segments in vessel segmentation by taking advantage of the vessel graph and determining the landmark points on it.…”

Section: Post Processingmentioning

confidence: 99%

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Analyzing morphological alternations of vessels in multiple Sclerosis using SLO images of the eyes

Soltanipour,

Arian,

Aghababaei

et al. 2023

Preprint

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BackgroundOur purpose was to investigate the most relevant and discriminating clinical feature set of Scanning laser ophthalmoscopy (SLO) images, which could differentiate multiple sclerosis (MS) and healthy control (HC) patients.MethodsIn this work, SLO images were used for the first time to measure the most valuable manual and clinical features from some retinal structures, optic disc, cup and blood vessels, for MS and HC classifications. For this, first an age-matching algorithm along with a subject-wise k-fold cross-validation data splitting approach were applied for construction of training, validation and test dataset, minimizing the risk of model overestimation. Then, it was needed to segment the retinal structures from the SLO images, and due to the lack of ground truth for our SLO images, we took advantage of a previously proposed deep learning algorithm for anatomical segmentation using color fundus images. But owing to different imaging modalities of SLO images, we also used two stages of pre-processing and post-processing to obtain accurate results for the segmentation step. Following that, a set of manual and clinical features was measured from the segmented optic disc, cup and vessels to gain a better comprehension of the features playing an important role in classification of MS and HC images. Finally, three simple machine learning models were applied to evaluate the measured features and the most valuable and effective features were computed.ResultsThe measured feature set from the segmented optic disc, cup and blood vessels resulted in a mean accuracy (ACC) of 83%, sensitivity (SE) of 79%, specificity (SP) of 85%, and AUROC of 84%, when testing on validation data by using a XGBoost classifier model. Furthermore, horizontally disc location, fractal dimension and intensity variation of blood vessels were selected as the most important and effective features for MS and HC classification.ConclusionThe location of optic disc, fractal dimension and vessel intensity, the ratio between intensity of vessels to intensity of he whole SLO image, were selected as three most valuable features for MS and HC classification. Regarding the optic disc location, we found out the used SLO images had been captured with two different imaging techniques. So, this feature could not be trusted as the most important feature. Two other features were confirmed by one expert as clinically distinguishing features for MS and HC classification.

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Section: Post Processingmentioning

confidence: 99%

Section: Post Processingmentioning

confidence: 99%

Analyzing morphological alternations of vessels in multiple Sclerosis using SLO images of the eyes

Soltanipour,

Arian,

Aghababaei

et al. 2023

Preprint

View full text Add to dashboard Cite

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The RETA Benchmark for Retinal Vascular Tree Analysis

2022

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Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

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Construction of retinal vascular trees via curvature orientation prior

Cited by 2 publications

References 29 publications

Analyzing morphological alternations of vessels in multiple Sclerosis using SLO images of the eyes

Analyzing morphological alternations of vessels in multiple Sclerosis using SLO images of the eyes

The RETA Benchmark for Retinal Vascular Tree Analysis

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