A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation
This work addresses the problem of segmenting irregular and fuzzy skin lesions for medical analysis, representing an incremental improvement over existing methods.
The paper tackled skin lesion segmentation by proposing Triple-UNet, a combination of three UNet architectures with a region of interest enhancement module, and reported that it outperforms state-of-the-art methods on a public dataset.
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.