Fengying Xie1, Jiawen Yang1, Jie Liu2, Zhiguo Jiang1, Yushan Zheng1, Yukun Wang3. 1. Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China. 2. Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China. Electronic address: Liujie04672@pumch.cn. 3. Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Abstract
BACKGROUND AND OBJECTIVE: Skin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation methods based on convolutional neural networks often fail to extract accurate lesion boundaries because the spatial size of feature maps decreases as the maps are processed throughout the network layers. We propose skin lesion segmentation in dermoscopy images based on a convolutional neural network with an attention mechanism, which can preserve edge details. METHODS: We devised a high-resolution feature block containing three branches, namely, main, spatial attention, and channel-wise attention branches. The main branch takes high-resolution feature maps as input to extract spatial details around boundaries. The other two attention branches boost the discriminative features in the main branch regarding the spatial and channel-wise dimensions. By fusing the branch outputs, robust features with detailed spatial information can be extracted, and accurate skin lesion boundaries can be obtained. RESULTS: Experiments on datasets from the International Symposium on Biomedical Imaging in 2016 and 2017 and the PH2 dataset retrieved Jaccard indices of 0.783, 0.858, and 0.857, respectively, for the proposed method. Hence, our method can accurately extract skin lesion boundaries and is robust to hair fibers and artifacts in the images. Overall, our method outperforms two typical segmentation networks (FCN-8 s and U-Net) and other state-of-the-art skin lesion segmentation methods. CONCLUSIONS: The proposed network endowed with high-resolution feature blocks preserves spatial details during feature extraction, and its attention mechanism enhances representative features while suppressing noise. Hence, the proposed approach provides high-performance skin lesion segmentation.
BACKGROUND AND OBJECTIVE:Skin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation methods based on convolutional neural networks often fail to extract accurate lesion boundaries because the spatial size of feature maps decreases as the maps are processed throughout the network layers. We propose skin lesion segmentation in dermoscopy images based on a convolutional neural network with an attention mechanism, which can preserve edge details. METHODS: We devised a high-resolution feature block containing three branches, namely, main, spatial attention, and channel-wise attention branches. The main branch takes high-resolution feature maps as input to extract spatial details around boundaries. The other two attention branches boost the discriminative features in the main branch regarding the spatial and channel-wise dimensions. By fusing the branch outputs, robust features with detailed spatial information can be extracted, and accurate skin lesion boundaries can be obtained. RESULTS: Experiments on datasets from the International Symposium on Biomedical Imaging in 2016 and 2017 and the PH2 dataset retrieved Jaccard indices of 0.783, 0.858, and 0.857, respectively, for the proposed method. Hence, our method can accurately extract skin lesion boundaries and is robust to hair fibers and artifacts in the images. Overall, our method outperforms two typical segmentation networks (FCN-8 s and U-Net) and other state-of-the-art skin lesion segmentation methods. CONCLUSIONS: The proposed network endowed with high-resolution feature blocks preserves spatial details during feature extraction, and its attention mechanism enhances representative features while suppressing noise. Hence, the proposed approach provides high-performance skin lesion segmentation.