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The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers.

Lijie Deng1, Junyan Lyu1, Haixiang Huang2

  • 1Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.

Scientific Data
|January 22, 2020
PubMed
Summary
This summary is machine-generated.

A new dataset of 712 ocular staining images with corneal ulcer segmentation labels and classification data is now available. This resource aids in developing advanced AI for diagnosing and grading corneal ulcers.

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Area of Science:

  • Ophthalmology and Artificial Intelligence
  • Medical Image Analysis

Background:

  • Corneal ulcer diagnosis requires accurate segmentation of ocular staining images.
  • Development of automated segmentation algorithms is hindered by a lack of high-quality, labeled datasets.

Purpose of the Study:

  • To introduce a novel dataset for corneal ulcer segmentation and classification.
  • To facilitate research in AI-driven ophthalmic diagnostics, particularly for flaky corneal ulcers.

Main Methods:

  • Compiled a dataset of 712 ocular staining images.
  • Provided pixel-level segmentation masks for flaky corneal ulcers.
  • Included multi-level classification labels: general pattern, specific pattern, and severity degree.

Main Results:

  • The dataset offers comprehensive annotations for corneal ulcer analysis.
  • Enables investigation of segmentation and classification algorithm performance.
  • Supports the advancement of deep learning models for ophthalmic imaging.

Conclusions:

  • The presented dataset is a valuable resource for advancing corneal ulcer detection and grading.
  • It will accelerate the development of more accurate and reliable AI-based diagnostic tools in ophthalmology.