@inproceedings{36d063d6da004fb3851479efd09c289f,
title = "A Novel Multi-Focus Fusion Network for Retinal Microsurgery",
abstract = "Retinal microsurgery requires high precision. Due to the limited depth of field (DOF) of the ophthalmic microscope and eyeball's spherical construction, doctors observe the retina with partially in focus and partly out of focus. To solve this problem, we propose a deep-learning-based multi-focus fusion model to reconstruct an all-in-focus image. A focus mea-sure block (FMB) is proposed to obtain the focus area in an image, and a fusion network (FN) is adopted to fuse the selected focus areas to produce the all-in-focus image. Considering the characteristics of retinal images, we propose to adopt two new losses to constrain our network. Based on our in-house dataset, extensive experiments prove the effectiveness of our algorithm.",
keywords = "Retinal microsurgery, deep-learning, depth of field, focus measure, fusion",
author = "Xinyi Zhou and Louying Hao and Qiushi Nie and Yingquan Zhou and Lihui Wang and Yan Hu and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 ; Conference date: 28-03-2022 Through 31-03-2022",
year = "2022",
doi = "10.1109/ISBI52829.2022.9761531",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "ISBI 2022 - Proceedings",
address = "United States",
}