A skin lesion hair mask dataset with fine-grained annotations

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

本数据集是目前规模最大的公开可用皮肤病变毛发分割掩码数据集，通过对从ISIC 2018数据集（ISIC 2018 dataset）[1]中采集的500张无版权CC0许可（CC0 license）皮肤镜图像（dermoscopic image）进行精细标注构建而成。
本数据集共分为三个文件夹，分别为皮肤镜图像文件夹（dermoscopic_image）、毛发掩码文件夹（hair_mask）以及叠加效果图文件夹（overlay）。
皮肤镜图像文件夹中包含500张精心挑选的、涵盖不同毛发形态的皮肤镜图像，我们保留了原始图像来源中的文件名。
毛发掩码文件夹中为皮肤镜图像文件夹内的每一张图像提供了对应的二值分割掩码图像。在分割掩码图像中，白色像素代表皮肤毛发，黑色像素代表背景。
叠加效果图文件夹中存放了将毛发掩码叠加至原始皮肤镜图像后的结果图像。我们提供此类叠加图像以便公众进行验证，方便他人反馈标注错误，助力本数据集的优化完善。
毛发掩码文件夹与叠加效果图文件夹内的图像文件名，与皮肤镜图像文件夹中的原始图像文件名保持一致。
附加材料文件夹（additional_materials）中存放了用于构建本数据集的代码与相关辅助材料，该文件夹包含以下内容：
- 在unet文件夹中，U-net（U-Net）模型的定义代码存放于model.py文件中；U-net模型的训练通过unet_training.ipynb Python笔记本文件完成。针对皮肤镜图像的初始掩码预测任务，则通过predict_mask.ipynb文件实现。
- 用于掩码二值化、设置掩码透明度以及生成图像拼贴画的代码，存放于check_annotation.ipynb文件中。
- 毛发掩码编辑流程的视频演示文件为mask_editing_process.mp4。
参考文献
[1] Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, et al. 面向黑色素瘤检测的皮肤病变分析2018：国际皮肤影像协作组（ISIC）主办的挑战赛[EB/OL]. 2019. https://doi.org/10.48550/arxiv.1902.03368.
[2] Ronneberger O, Fischer P, Brox T. U-Net：用于生物医学图像分割的卷积网络[C]//Navab N, Hornegger J, Wells WM, Frangi AF, eds. 医学图像计算与计算机辅助干预——MICCAI 2015. Cham：施普林格国际出版公司; 2015: 234–241. https://doi.org/10.1007/978-3-319-24574-4_28.