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

• Published in: Scientific data Vol. 7; no. 1; p. 23
• Main Authors: Deng, Lijie, Lyu, Junyan, Huang, Haixiang, Deng, Yuqing, Yuan, Jin, Tang, Xiaoying
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.01.2020; Nature Publishing Group
• Subjects: 631/114/1314; 631/1647/245/2225; 692/699/3161/3163; Algorithms; Cornea; Corneal Ulcer - diagnostic imaging; Data Descriptor; Datasets; Deep learning; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; multidisciplinary; Reproducibility of Results; Science; Science (multidisciplinary); Segmentation; Ulcers
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0360-7

Corneal ulcer is a common ophthalmic symptom. Segmentation algorithms are needed to identify and quantify corneal ulcers from ocular staining images. Developments of such algorithms have been obstructed by a lack of high quality datasets (the ocular staining images and the corresponding gold-standard ulcer segmentation labels), especially for supervised learning based segmentation algorithms. In such context, we prepare a dataset containing 712 ocular staining images and the associated segmentation labels of flaky corneal ulcers. In addition to segmentation labels for flaky corneal ulcers, we also provide each image with three-fold class labels: firstly, each image has a label in terms of its general ulcer pattern; secondly, each image has a label in terms of its specific ulcer pattern; thirdly, each image has a label indicating its ulcer severity degree. This dataset not only provides an excellent opportunity for investigating the accuracy and reliability of different segmentation and classification algorithms for corneal ulcers, but also advances the development of new supervised learning based algorithms especially those in the deep learning framework. Measurement(s) corneal ulcer Technology Type(s) staining Factor Type(s) ulcer severity • ulcer pattern Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11371803