文献类型：期刊文献

英文题名：M3U-CDVAE: Lightweight retinal vessel segmentation and refinement network

作者：Yu, Yang[1];Zhu, Hongqing[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：79

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20223512629984);WOS:【SCI-EXPANDED(收录号:WOS:000861391700004)】；

基金：Funding This work was supported by the National Nature Science Foundation of China under Grant 61872143.

语种：英文

外文关键词：Retinal blood vessel segmentation; Lightweight network; Variational auto -encoder; Feature fusion; MobileNetV3

摘要：Retinal vessels have high curvature and diverse morphology, making them difficult to segment, especially tiny vessels. At present, the retinal vessels are mainly annotated manually by experts, which is difficult to meet the vast clinical needs. To solve the above problems, we propose an effective network M3U-CDVAE. It adopts the architecture of a segmentation-refinement network to denoise and optimizes segmentation results. Firstly, we design a lightweight segmentation network M3U with an encoder-decoder structure. Then, the Hierarchical Feature Fusion (HFF) unit combines the intermediate features generated by the segmentation network with the pre-segmentation results and connects them to the corresponding layer in the next sub-model. Finally, Con-volutional Denoising Variational Auto-Encoder (CDVAE) is used as the refinement network to remove the background noise and optimize segmentation results. We conduct exhaustive ablation experiments to demon-strate the improvement brought by our contribution. At the same time, we carry out comparison experiments on DRIVE, STARE, and HRF datasets to illustrate the effectiveness of the proposed method. Experimental results exhibit that the proposed method is superior to most state-of-art methods.