Multi-path cascaded U-net for vessel segmentation from fundus fluorescein angiography sequential images. (November 2021)

• Record Type: Journal Article
• Authors: Sun, Gang; Liu, Xiaoyan; Yu, Xuefei
• Is Part Of: Computer methods and programs in biomedicine. Volume 211(2021)
• Publication Date: 2021-11
• Subjects: Fluorescein angiography -- Sequential images -- Vessel segmentation -- Deep learning network -- U-net; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106422
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: MCU-net is proposed to segment sequential fundus fluorescein angiography (FFA) images MCU-net exploits vascular features by fusing information through a multi-path architecture MCU-net is more robust to FFA images in different perfusion stages MCU-net shows higher extendibility for FFA sequential images from different sources Abstract: Background and Objective: Fundus fluorescein angiography (FFA) technique is widely used in the examination of retinal diseases. In analysis of FFA sequential images, accurate vessel segmentation is a prerequisite for quantification of vascular morphology. Current vessel segmentation methods concentrate mainly on color fundus images and they are limited in processing FFA sequential images with varying background and vessels. Methods: We proposed a multi-path cascaded U-net (MCU-net) architecture for vessel segmentation in FFA sequential images, which is capable of integrating vessel features from different image modes to improve segmentation accuracy. Firstly, two modes of synthetic FFA images that enhance details of small vessels and large vessels are prepared, and are then used together with the raw FFA image as inputs of the MCU-net. By fusion of vessel features from the three modes of FFA images, a vascular probability map is generated as output of MCU-net. Results: The proposed MCU-net was trained and tested on the public Duke dataset and our own dataset for FFA sequential images as well as on the DRIVE dataset for color fundus images. Results show that MCU-net outperforms current state-of-the-art methods in terms of F1-score, sensitivity and accuracy, and is able of reserving details such as thin vessels and vascular connections. It also shows good robustness in processing FFA images captured at different perfusion stages. Conclusions: The proposed method can segment vessels from FFA sequential images with high accuracy and shows good robustness to FFA images in different perfusion stages. This method has potential applications in quantitative analysis of vascular morphology in FFA sequential images.