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.

翻译：皮肤病变分割对于皮肤病变分析及后续治疗具有重要意义。由于病变边界不规则、模糊且皮肤病变具有多样性，该任务仍具挑战性。本文提出Triple-UNet以实现皮肤病变自动分割，该架构是三个UNet子网络与适配模块的有机融合。为更有效地连接第一与第二子网络，我们设计了感兴趣区域增强模块（ROIE）。该模块利用第一UNet的预测得分图增强图像的目标区域。第一UNet学习的特征与增强后的图像共同助力第二UNet获得更优的得分图。最终由第三UNet对结果进行精细调整。我们在公开皮肤病变分割数据集上评估了算法性能。实验表明，Triple-UNet在皮肤病变分割任务上优于现有最优方法。