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Artery/Veins Retinal Vessel Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
5
보존된 평가 기록
9

Artery/Veins (A/V) Retinal vessel segmentation is the task of segmenting vessels in retinal images, while distinguishing between arterioles and venules.

데이터셋

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HRF

The HRF dataset is a dataset for retinal vessel segmentation which comprises 45 images and is organized as 15 subsets. Each subset contains one healthy fundus image, one image of patient with diabetic retinopathy and one glaucoma image. The image sizes are 3,304 x 2,336, with a training/testing image split of 22/23. Source: Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation Image Source: https://www.researchgate.net/figure/Examples-of-fundus-images-from-HRF-database-with-corresponding-hand-labelled-gold_fig1_260625531

UZLF

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

INSPIRE-AVR (LUNet subset)

This dataset contains 65 DFIs acquired from patients with POAG at the University of Iowa Hospitals and Clinics. DFIs were acquired using a 30° Zeiss fundus camera (Niemeijer et al 2011). The images were centered on the optic disc. The original DFIs resolution was 2392 × 2048. In order to benchmark LUNet on this dataset, the black border of the DFIs were padded to a squared resolution of 2048 × 2048 pixels and then resized to a 1444 × 1444 pixels resolution. From the resulting DFIs, 15 optic disc-centered DFIs were randomly selected to form the second external test set. No other additional metadata were provided in the open source dataset.

LES-AV

This data set comprises 22 fundus images with their corresponding manual annotations for the blood vessels, separated as arteries and veins. It also include labels for glaucomatous / healthy, differentiating between normal tension glaucoma (NAG) and primary open angle glaucoma (POAG). Further information about the data set is provided in our MICCAI 2018 paper: Orlando, J. I., Breda, J. B., van Keer, K., Blaschko, M. B., Blanco, P. J., & Bulant, C. A. (2018). Towards a glaucoma risk index based on simulated hemodynamics from fundus images. MICCAI 2018. Please, cite the corresponding paper in case you use this data on a scientific publication. Any commercial usage is forbidden. This data set can only be used for scientific purposes.

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