Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images
文献类型：会议论文
| 作者 | Mao, Xiaoqian; Zhao, Yitian; Chen, Bang; Ma, Yuhui; Gu, Zaiwang; Gu, Shenshen; Yang, Jianlong; Cheng, Jun; Liu, Jiang |
| 出版日期 | 2020 |
| 会议日期 | JUL 20-24, 2020 |
| 英文摘要 | Since the thickness and shape of the choroid layer are indicators for the diagnosis of several ophthalmic diseases, the choroid layer segmentation is an important task. There exist many challenges in segmentation of the choroid layer. In this paper, in view of the lack of context information due to the ambiguous boundaries, and the subsequent inconsistent predictions of the same category targets ascribed to the lack of context information or the large regions, a novel Skip Connection Attention (SCA) module which is integrated into the U-Shape architecture is proposed to improve the precision of choroid layer segmentation in Optical Coherence Tomography (OCT) images. The main function of the SCA module is to capture the global context in the highest level to provide the decoder with stage-by-stage guidance, to extract more context information and generate more consistent predictions for the same class targets. By integrating the SCA module into the U-Net and CE-Net, we show that the module improves the accuracy of the choroid layer segmentation. |
| 会议录出版者 | IEEE Engineering in Medicine and Biology Society Conference Proceedings |
| 学科主题 | Engineering |
| ISSN号 | 1557-170X |
| ISBN号 | 978-1-7281-1990-8 |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/23279]  |
| 专题 | 会议专题 会议专题_会议论文 |
| 推荐引用方式 GB/T 7714 | Mao, Xiaoqian,Zhao, Yitian,Chen, Bang,et al. Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images[C]. 见:. JUL 20-24, 2020. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。