A skin lesion hair mask dataset with fine-grained annotations

Description

The largest publicly available skin lesion hair segmentation mask dataset created by carefully annotating 500 copyright-free CC0 licensed dermoscopic images collected from ISIC 2018 dataset [1]. The dataset is organized into three folders namely dermoscopic_image, hair_mask, and overlay. The dermoscopic_image folder contains 500 handpicked dermoscopic images covering different hair patterns. We retained the original names of the image files from the primary image source. The hair_mask folder contains a binary segmentation mask for each of the images of the dermoscopic_image folder. In a segmentation mask image, white pixels represent skin hair and black pixels represent background. The overlay folder contains hair mask images superimposed on the original dermoscopic images. We provided the superimposed images for easy public verification so that, other people can report any annotation mistakes and contribute to improving the dataset. Images in the hair_mask and overlay folders share the same names as the primary images in the dermoscopic_image folder. additional_materials folder contains codes and additional materials used for preparing the dataset. additional_materials folder contents: - Inside the unet folder the U-net [2] model is defined in model.pyfile, unet training is performed using the unet_training.ipynb python notebook file. The task of predicting initial masks for the dermoscopic images is done using the predict_mask.ipynbfile. - The codes used for binarizing mask, making it transparent and creating image collage are available in the check_annotation.ipynbfile. - Video demonstration of the hair mask editing process is available in the mask_editing_process.mp4 file. References [1] Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, et al. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) 2019. https://doi.org/10.48550/arxiv.1902.03368. [2] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Med. Image Comput. Comput. Interv. -- MICCAI 2015, Cham: Springer International Publishing; 2015, p. 234–41. https://doi.org/10.1007/978-3-319-24574-4_28

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