TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset
Jingxin Mao Xiaoyu Ma Yanlong Bi Rongqing Zhang
Diabetic retinopathy (DR), as a debilitating ocular complication,
necessitates prompt intervention and treatment. Despite the effectiveness of
artificial intelligence in aiding DR grading, the progression of research
toward enhancing the interpretability of DR grading through precise lesion
segmentation faces a severe hindrance due to the scarcity of pixel-level
annotated DR datasets. To mitigate this, this paper presents and delineates
TJDR, a high-quality DR pixel-level annotation dataset, which comprises 561
color fundus images sourced from the Tongji Hospital Affiliated to Tongji
University. These images are captured using diverse fundus cameras including
Topcon's TRC-50DX and Zeiss CLARUS 500, exhibit high resolution. For the sake
of adhering strictly to principles of data privacy, the private information of
images is meticulously removed while ensuring clarity in displaying anatomical
structures such as the optic disc, retinal blood vessels, and macular fovea.
The DR lesions are annotated using the Labelme tool, encompassing four
prevalent DR lesions: Hard Exudates (EX), Hemorrhages (HE), Microaneurysms
(MA), and Soft Exudates (SE), labeled respectively from 1 to 4, with 0
representing the background. Significantly, experienced ophthalmologists
conduct the annotation work with rigorous quality assurance, culminating in the
construction of this dataset. This dataset has been partitioned into training
and testing sets and publicly released to contribute to advancements in the DR
lesion segmentation research community.