IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset
This dataset addresses a critical bottleneck for researchers in medical image segmentation by providing a resource to study annotator variability and model preferences, though it is incremental as it builds on existing archives.
The authors tackled the lack of large-scale multi-annotator datasets for dermoscopic skin lesion segmentation by introducing ISIC MultiAnnot++, which contains 17,684 masks across 14,967 images, including 2,394 images with 2-5 segmentations each, making it the largest publicly available dataset in this domain.
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.