IMA++：ISIC档案多标注者皮肤镜色素性皮损分割数据集

摘要

多标注者医学图像分割是一项重要的研究课题，但需要耗费高昂成本才能获取标注数据集。皮肤镜病灶成像技术使得人类专家和人工智能系统能够观察到常规临床照片无法辨别的形态学结构。然而目前尚缺乏包含标注者标签的大规模公开多标注者皮肤镜病灶分割数据集。我们推出ISIC MultiAnnot++——一个基于ISIC档案库图像的大型公开多标注者皮肤病灶分割数据集。该最终数据集包含17,684个分割掩模，覆盖14,967张皮肤镜图像，其中2,394张图像每张包含2-5个分割标注，使其成为当前最大的公开SLS数据集。此外，数据集还包含关于分割的元数据（包括标注者技能水平和分割工具），可支持标注者特异性偏好建模、标注者元数据分析等课题研究。我们对该数据集特征、整理的数据分区及共识分割掩模进行了分析。

English

Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows human experts and AI systems to observe morphological structures otherwise not discernable from regular clinical photographs. However, currently there are no large-scale publicly available multi-annotator skin lesion segmentation (SLS) datasets with annotator-labels for dermoscopic skin lesion imaging. We introduce ISIC MultiAnnot++, a large public multi-annotator skin lesion segmentation dataset for images from the ISIC Archive. The final dataset contains 17,684 segmentation masks spanning 14,967 dermoscopic images, where 2,394 dermoscopic images have 2-5 segmentations per image, making it the largest publicly available SLS dataset. Further, metadata about the segmentation, including the annotators' skill level and segmentation tool, is included, enabling research on topics such as annotator-specific preference modeling for segmentation and annotator metadata analysis. We provide an analysis on the characteristics of this dataset, curated data partitions, and consensus segmentation masks.

IMA++：ISIC档案多标注者皮肤镜色素性皮损分割数据集

IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset

摘要

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