A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers

Kovács, György; Fazekas, Attila

doi:10.1016/j.media.2021.102300

Cited by 12 publications

(11 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fundus mask, also known as the FOV mask, refers to a binary image indicating all captured retinal pixels by the fundus camera. It is reported that unbiased model performance had better be evaluated only in the FOV region 46 . The fundus and vessel masks are saved in PNG format (JPG format is a terrible option for saving binary images).…”

Section: Data Recordsmentioning

confidence: 99%

See 1 more Smart Citation

The RETA Benchmark for Retinal Vascular Tree Analysis

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Data Recordsmentioning

confidence: 99%

“…A golden combination set {accuracy, sensitivity and specificity} is frequently adopted to indicate vessel segmentation performance in literature 9 , 46 . Other pixel-wise matching based metrics ( e.g .…”

Section: Usage Notesmentioning

confidence: 99%

The RETA Benchmark for Retinal Vascular Tree Analysis

2022

View full text Add to dashboard Cite

show abstract

“…In addition, as shown in the previous series of experiments, with the help of the multi-scale context information in the MCG module, the proposed MCG&BA-Net is noise robust. In the experiment, the size of the multiscale contextual feature map is empirically set as (1,2,3,6). However, for images under different background environments, if the feature scale is appropriately enlarged (reduced size) for larger noises and lesions, and the feature scale is appropriately reduced (increased size) for smaller noises and lesions, better segmentation results will be achieved theoretically.…”

Section: Robustness To Background Noises and Retinal Abnormalitiesmentioning

confidence: 99%

“…A large number of automated segmentation approaches can be found in the literature [6]. Traditional segmentation methods include unsupervised segmentation methods, such as vessel tracking, gradients‐based edge/contour detection, and supervised methods, involving a feature extraction stage and a feature classification stage.…”

Section: Introductionmentioning

confidence: 99%

MCG&BA‐Net: Retinal vessel segmentation using multiscale context gating and breakpoint attention

Zhao

Liu

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

The accurate segmentation of blood vessels plays a crucial role in screening, diagnosis and treatment of multiple diseases. However, current automated segmentation approaches do not pay enough attention to the vascular topology errors (such as mistaking vesselbreakpoints), resulting in considerable scattered vessel-fragments in segmentation results. This article proposes a retinal vessel segmentation model using multi-scale context gating and breakpoint attention mechanism, called MCG&BA-Net. Specifically, it obtains a feature map containing contextual information of vessels through an introduced multi-scale context module, and then filters the redundant features and noises by a gated structure to highlight target features. Furthermore, a kind of breakpoint attention module is proposed, which can locate and focus on potential breakpoint areas, thereby facilitating accurate segmentation results of tree-like fine vessels. Extensive confirmatory and comparative experiments have been conducted on five public datasets, including three benchmark datasets, that is, DRIVE, CHASDB1 and SATRE, and two clinical datasets, that is, fun-dusimage1000 and RFMID. The AUC scores on the benchmark datasets are 0.9878, 0.9923 and 0.9942, respectively. Among them, the AUC score on CHADEDB1 and STARE outperforms the state-of-the-art results. In addition, experimental results on the two clinical datasets demonstrate strong generalization capability of the propose method, indicating high clinical application values.

show abstract

“…Dash et al suggested a hybrid technique for the extraction of thin and thick vessels [46]. Kovacs and Fazekas recommended a new baseline for blood vessel segmentation [47]. Dash and Senapati improved the performance of the Coye filter by integrating it with discrete wavelet transform (DWT) for vessel extraction [48].…”

Section: Introductionmentioning

confidence: 99%

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

et al. 2022

View full text Add to dashboard Cite

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

show abstract

A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers

Cited by 12 publications

References 94 publications

The RETA Benchmark for Retinal Vascular Tree Analysis

The RETA Benchmark for Retinal Vascular Tree Analysis

MCG&BA‐Net: Retinal vessel segmentation using multiscale context gating and breakpoint attention

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Contact Info

Product

Resources

About