Skin lesion segmentation is of great significance for skin lesion analysis and subsequent treatment. It is still a challenging task due to the irregular and fuzzy lesion borders, and diversity of skin lesions. In this paper, we propose Triple-UNet to automatically segment skin lesions. It is an organic combination of three UNet architectures with suitable modules. In order to concatenate the first and second sub-networks more effectively, we design a region of interest enhancement module (ROIE). The ROIE enhances the target object region of the image by using the predicted score map of the first UNet. The features learned by the first UNet and the enhanced image help the second UNet obtain a better score map. Finally, the results are fine-tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments show that Triple-UNet outperforms the state-of-the-art on skin lesion segmentation.

本文提出了一种名为Triple-UNet的算法，用于自动分割皮肤病变。该算法将三个UNet架构进行有机结合，并引入了区域增强模块(ROIE)，通过利用第一个UNet的预测评分图增强图像中的目标对象区域。通过第一个UNet学习到的特征和增强后的图像，帮助第二个UNet获得更好的评分图。最终，通过第三个UNet对结果进行微调。实验证明，Triple-UNet在皮肤病变分割方面优于现有技术。

一个专注于感兴趣区域的三重UNet架构用于皮肤病变分割