A Region of Interest Focused Triple UNet Architecture for Skin Lesion
  Segmentation
Guoqing Liu Yu Guo Caiying Wu Guoqing Chen Barintag Saheya Qiyu Jin
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.