计算机科学与探索2024，Vol.18Issue(7)：1826-1837,12.DOI:10.3778/j.issn.1673-9418.2306003
多尺度和边界融合的皮肤病变区域分割网络
Multi-scale and Boundary Fusion Network for Skin Lesion Regions Segmentation
摘要
Abstract
Accurate segmentation of skin lesion regions is a key step in clinical diagnosis and analysis.Aiming at the poor segmentation effect of the existing networks in skin lesion regions due to the presence of variable size,ir-regular shape,fuzzy boundaries and obscured lesion regions,a multi-scale and boundary fusion network(MSBF-Net)for skin lesion region segmentation is proposed by improving the original structure based on the U-Net.Specifi-cally,firstly,a split pooling(SplitPool)module is proposed,which effectively solves the problem of spatial informa-tion loss while reducing the image resolution.Secondly,a full-scale feature fusion(FSFF)module is proposed,which effectively solves the problem that the previous methods only fuse the deep features to the shallow features,while ignoring the contribution of the detail information in the more shallow features to the network segmentation decision.Meanwhile,the original jump connections of U-Net are redesigned to provide richer contextual informa-tion for the decoder.Finally,sub-paths for enhancing the network's ability to learn boundary features are proposed,and the boundary fusion(BF)module is introduced to fuse the prediction results of the main paths and sub-paths,which effectively solves the problems of irregular shape and boundary ambiguity of the lesion region.Dice and JI reach 90.12% and 83.61% on the ISIC2018 dataset,which are 1.13 percentage points and 1.62 percentage points higher than the baseline network,respectively;Dice and JI reach 94.72% and 90.18% on the PH2 dataset,which are 1.49 percentage points and 2.17 percentage points higher than the baseline network,respectively.Experimental re-sults show that MSBF-Net significantly improves the accuracy of skin lesion region segmentation and exceeds the existing state-of-the-art methods in several indices,further validating the effectiveness of the method.关键词
皮肤病变区域分割/跳跃连接/边界特征/特征融合/注意力机制Key words
skin lesion region segmentation/skip connections/boundary feature/feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
王国凯,张翔,王顺芳..多尺度和边界融合的皮肤病变区域分割网络[J].计算机科学与探索,2024,18(7):1826-1837,12.基金项目
国家自然科学基金(62062067) （62062067）
云南省智能系统与计算重点实验室建设项目(202205AG070003).This work was supported by the National Natural Science Foundation of China(62062067),and the Construction Project of Yunnan Key Laboratory of Intelligent Systems and Computing(202205AG070003). （202205AG070003）
