Knowledge Commons of Institute of Automation,CAS
DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation | |
Changwei Wang1,3![]() ![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | Medical Image Computing and Computer Assisted Intervention |
会议日期 | 2022 |
会议地点 | 新加坡 |
摘要 | Since the morphology of retinal vessels plays a pivotal role in clinical diagnosis of eye-related diseases and diabetic retinopathy, retinal vessels segmentation is an indispensable step for the screening and diagnosis of retinal diseases, yet it is still a challenging problem due to the complex structure of retinal vessels. Current retinal vessels segmentation approaches roughly fall into image-level and patches-level methods based on the input type, while each has its own strengths and weaknesses. To benefit from both of the input forms, we introduce a Dual Branch Transformer Module (DBTM) that can simultaneously and fully enjoy the patches-level local information and the image-level global context. Besides, the retinal vessels are long-span, thin, and distributed in strips, making the square kernel of classic convolutional neural network false as it is only suitable for most natural objects with bulk shape. To better capture context information, we further design an Adaptive Strip Upsampling Block (ASUB) to adapt to the striped distribution of the retinal vessels. Based on the above innovations, we propose a retinal vessels segmentation Network with Dual Branch Transformer and Adaptive Strip Upsampling (DA-Net). Experiments validate that our DA-Net outperforms other state-of-the-art methods on both DRIVE and CHASE-DB1 datasets. |
收录类别 | EI |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56649 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Shibiao Xu; Weiliang Meng |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
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 | Changwei Wang,Rongtao Xu,Shibiao Xu,et al. DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
DANet.pdf(3869KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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