OCTA-500

Citation Author(s):
Mingchao
Li
Songtao
Yuan
Qiang
Chen
Submitted by:
Yerui Chen
Last updated:
Wed, 02/21/2024 - 05:02
DOI:
10.1016/j.media.2024.103092
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License:
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Keywords:
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Abstract 

OCTA-500

Optical coherence tomography angiography (OCTA) is a retinal imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.

We propose a new dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / disease) and seven types of segmentation labels (large vessel / capillary / artery / vein / 2D FAZ / 3D FAZ / retinal layers). 

 

Get Password:

To get the password of the compressed package, an application email must be sent to  chen2qiang@njust.edu.cn with a specified form like below, otherwise may be ignored.

  • Title of Mail: 
    • OCTA500: your_organization: your_name

Note that: The string of 'OCTA500' can not be empty. It is the fixed form and a special sign we use to identifying your downloading intention from other disturbers like spams.

  • Body of Mail:
    • Organization Detail: Your Organization Details
    • Main Works: Your Main Works
    • Usages: YourUsages About This Data Set

 

Related Papers:

Dataset:

  • Mingchao Li, Kun Huang, Qiuzhuo Xu, Jiadong Yang, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, and Qiang Chen. "OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study," Medical Image Analysis, 2024: 103092.

Vessel segmentation:

  • Mingchao Li, Yerui Chen, Zexuan Ji, Keren Xie, Songtao Yuan, Qiang Chen, and Shuo Li."Image projection network: 3D to 2D image segmentation in OCTA images," IEEE Trans. Med. Imaging, vol.39, no.11, pp.3343-3354, 2020.
  • Mingchao Li, Weiwei Zhang, and Qiang Chen. "Image magnification network for vessel segmentation in OCTA images," in Chinese Conference on Pattern Recognition and Computer Vision. arXiv:2110.13428, 2022.
  • Mingchao Li, Kun Huang, Zetian Zhang, Xiao Ma, and Qiang Chen. "Label adversarial learning for skeleton-level to pixel-level adjustable vessel segmentation," arXiv: 2205.03646, 2022.

Layer segmentation:

  • Yuhan Zhang, Chen Huang, Mingchao Li, Sha Xie, Keren Xie, Songtao Yuan, and Qiang Chen. "Robust layer segmentation against complex retinal abnormalities for en face OCTA generation," in MICCAI, 2020.
  • Jiadong Yang, Yuhui Tao, Qiuzhuo Xu,Yuhan Zhang, Xiao Ma, Songtao Yuan, and Qiang Chen. "Self-supervised sequence recovery for semi-supervised retinal layer segmentation," IEEE Journal of Biomedical and Health Informatics, vol.26, no.8, pp.3872-3883, 2022.

FAZ segmentation:

  • Qiuzhuo Xu, Weiwei Zhang, Hongjing Zhu, and Qiang Chen. "Foveal avascular zone volume: a new index based on optical coherence tomography angiography images," Retina, vol.41, no.3, pp.595-601, 2021.
  • Qiuzhuo Xu, Mingchao Li, Nairong Pan, Qiang Chen, and Weiwei Zhang. "Priors-guided convolutional neural network for 3D foveal avascular zone segmentation," Optics Express, vol.30, no.9, pp.14723-14736, 2022.

Dataset Update Log: 

  • [2020.10] OCTA-500 was released, including labels for Large Vessels and FAZ
  • [2021.3] Added Capillary labels
  • [2022.3] Added 3D FAZ labels
  • [2022.7] Added Artery-Vein labels  and Layer segmentation labels
  • [2023.10] Optimized Capillary labels
  • [2023.11] Optimized Layer segmentation labels

Dataset Structure:

OCTA-500 includes two subsets: OCTA_6M and OCTA_3M.

OCTA_6M(No.10001-No.10300):

  • FOV: 6mm*6mm*2mm
  • Volume: 400pixel*400pixel*640pixel

OCTA_3M(No.10301-No.10500):

  • FOV: 3mm*3mm*2mm
  • Volume: 304pixel*304pixel*640pixel

Both subsets contain the following information:

  • OCT volumes
  • OCTA volumes
  • Projection Maps
    1. OCT FULL(average)
    2. OCT ILM_OPL (average)
    3. OCT OPL_BM (average)
    4. OCTA FULL (average)
    5. OCTA ILM_OPL (maximum)
    6. OCTA OPL_BM (maximum)
  • Text Label
    1. Gender
    2. Age
    3. OS/OD
    4. Disease
  • Segmentation Label
    1. Large Vessel
    2. Artery
    3. Vein
    4. Capillary
    5. 2D FAZ
    6. 3D FAZ
    7. Retinal Layer
Instructions: 

 

By using the OCTA-500 dataset, you are obliged to reference at least one of the following papers:

-Mingchao Li, Kun Huang, Qiuzhuo Xu, Jiadong Yang, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, and Qiang Chen. "OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study," Medical Image Analysis, 2024: 103092.

-Mingchao Li, Yerui Chen, Zexuan Ji, Keren Xie, Songtao Yuan, Qiang Chen, and Shuo Li."Image projection network: 3D to 2D image segmentation in OCTA images," IEEE Trans. Med. Imaging, vol.39, no.11, pp.3343-3354, 2020.

Comments

Very good dataset

Submitted by Chubin Ou on Sat, 06/06/2020 - 03:36

Thank you for your comments

Submitted by Yerui Chen on Mon, 12/14/2020 - 21:38

sir whats the password of the zip files ?

Submitted by Nilanjan Pal on Mon, 09/14/2020 - 16:59

OCTA500 has been publicly released, please send an email to chaosli@njust.edu.cn to obtain permission and password.

Submitted by Yerui Chen on Mon, 12/14/2020 - 21:41

Did you get the unzip password?

Submitted by Quanliang Ye on Thu, 02/29/2024 - 03:11

what is the password?

Submitted by Mohamed Farag on Sun, 10/04/2020 - 04:29

OCTA500 has been publicly released, please send an email to chaosli@njust.edu.cn to obtain permission and password.

Submitted by Yerui Chen on Mon, 12/14/2020 - 21:42

would it be possible for you to share with me the password for this dataset? thank you!

Submitted by Christine Lim on Fri, 12/04/2020 - 05:51

OCTA500 has been publicly released, please send an email to chaosli@njust.edu.cn to obtain permission and password.

Submitted by Yerui Chen on Mon, 12/14/2020 - 21:45

OCTA500 has been publicly released, please send an email to chaosli@njust.edu.cn to obtain permission and password.

Submitted by xiaoyan xu on Thu, 01/14/2021 - 23:24

OCTA_3M_OCTA_part4
The 10451 folder is missing

Submitted by Jacky Chin on Mon, 01/18/2021 - 12:44

This missing file has been added in OCTA_3M_OCTA_part4

Submitted by Yerui Chen on Tue, 01/26/2021 - 21:08

Hi Please share the password, I have sent you mail (asiddharth.20dr0175@mech.iitism.ac.in)

Submitted by Arun Udai on Mon, 01/03/2022 - 07:17

Hi, I would request you to provide the fundus of the patients for whose OCTA images have been acquired, if available. I have sent you a mail too regarding the same.

Submitted by Arun Udai on Tue, 03/01/2022 - 01:58

Must I subscribe to access the full datasets?
I can't find the OCTA images

Submitted by dilemma ray on Mon, 02/20/2023 - 08:40

I've sent you seveal emails,please reply me the password,thx.
From zenki463@gmail.com

Submitted by Howard Jiang on Mon, 05/29/2023 - 01:40

Please respond to to my mail, I sent a mail from my school email : ojeji1@morgan.edu

Submitted by Ojonugwa Ejiga Peter on Tue, 12/12/2023 - 19:38

would it be possible for you to share with me the password for this dataset? thank you

Submitted by Ray JAY on Sat, 01/13/2024 - 04:48

Could you please advise on where I might acquire the four types of text labels?

Submitted by Zhao yue on Fri, 01/26/2024 - 10:54

Please respond to to my mail, I sent a mail from my school email : 20qlye@stu.edu.cn

Submitted by Quanliang Ye on Wed, 02/28/2024 - 23:49

Kudus to researcher take part in this data collection

Submitted by zulfiqar Ali on Sun, 11/17/2024 - 02:44

Hey! This is Lingesh.
Can you please provide me with the access to the .zip files? I've already mailed you. Please do check it out
Thank you!

Submitted by Lingesh K V on Sat, 11/23/2024 - 12:47

Dataset Files

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