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CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images | |
Bo,Wang1,2; Shengpei,Wang1,2; Shuang,Qiu2; Wei,Wei1,2; Haibao,Wang1,2; Huiguang,He1,2,3 | |
发表期刊 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS |
2021-04 | |
卷号 | 25期号:4页码:1128-1138 |
摘要 | Blood vessel segmentation in fundus images is a critical procedure in the diagnosis of ophthalmic diseases. Recent deep learning methods achieve high accuracy in vessel segmentation but still face the challenge to segment the microvascular and detect the vessel boundary. This is due to the fact that common Convolutional Neural Networks (CNN) are unable to preserve rich spatial information and a large receptive field simultaneously. Besides, CNN models for vessel segmentation usually are trained by equal pixel level cross-entropy loss, which tend to miss fine vessel structures. In this paper, we propose a novel Context Spatial U-Net (CSU-Net) for blood vessel segmentation. Compared with the other U-Net based models, we design a two-channel encoder: a context channel with multi-scale convolution to capture more receptive field and a spatial channel with large kernel to retain spatial information. Also, to combine and strengthen the features extracted from two paths, we introduce a feature fusion module (FFM) and an attention skip module (ASM). Furthermore, we propose a structure loss, which adds a spatial weight to cross-entropy loss and guide the network to focus more on the thin vessels and boundaries. We evaluated this model on three public datasets: DRIVE, CHASE-DB1 and STARE. The results show that the CSU-Net achieves higher segmentation accuracy than the current state-of-the-art methods. |
关键词 | Fundus images blood vessel segmentation CSU-Net feature fusion structure loss |
收录类别 | SCI |
七大方向——子方向分类 | 医学影像处理与分析 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44912 |
专题 | 脑图谱与类脑智能实验室_神经计算与脑机交互 |
通讯作者 | Huiguang,He |
作者单位 | 1.the School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Research Center for Brain-Inspired Intelligence, the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.Center for Excellence in Brain Science and Intelligence Technology, the Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Bo,Wang,Shengpei,Wang,Shuang,Qiu,et al. CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2021,25(4):1128-1138. |
APA | Bo,Wang,Shengpei,Wang,Shuang,Qiu,Wei,Wei,Haibao,Wang,&Huiguang,He.(2021).CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,25(4),1128-1138. |
MLA | Bo,Wang,et al."CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 25.4(2021):1128-1138. |
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1.pdf(6892KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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