Bgman: Boundary-Prior-Guided Multi-scale Aggregation Network for skin lesion segmentation

Skin lesion segmentation is a fundamental task in the field of medical image analysis. Deep learning approaches have become essential tools for segmenting medical images, as their accuracy in effectively analyzing abnormalities plays a critical role in determining the ultimate diagnostic results. Because of the inherent difficulties presented by medical images, including variations in shapes and sizes, along with the indistinct boundaries between lesions and the surrounding backgrounds, certain conventional algorithms face difficulties in fulfilling the growing requirements for elevated accuracy in processing medical images. To enhance the performance in capturing edge features and fine details of lesion processing, this paper presents the Boundary-Prior-Guided Multi-Scale Aggregation Network for skin lesion segmentation (BGMAN). The proposed BGMAN follows a basic Encoder-Decoder structure, wherein the encoder network employs prevalent CNN-based architectures to capture semantic information. We propose the Transformer Bridge Block (TBB) and employ it to enhance multi-scale features captured by the encoder. The TBB strengthens the intensity of weak feature information, establishing long-distance relationships between feature information. In order to augment BGMAN's capability to identify boundaries, a boundary-guided decoder is designed, utilizing the Boundary Aware Block (BAB) and Cross Scale Fusion Block (CSFB) to guide the decoding learning process. BAB can acquire features embedded with explicit boundary information under the supervision of a boundary mask, while CSFB aggregates boundary features from different scales using learnable embeddings. The proposed method has been validated on the ISIC2016, ISIC2017, and PH2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PH<^>2$$\end{document} datasets. It outperforms current mainstream networks with the following results: F1 92.99 and IoU 87.71 on ISIC2016, F1 86.42 and IoU 78.34 on ISIC2017, and F1 94.83 and IoU 90.26 on PH2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PH<^>2$$\end{document}.