A Novel Multi-Focus Fusion Network for Retinal Microsurgery

Xinyi Zhou, Louying Hao, Qiushi Nie, Yingquan Zhou, Lihui Wang, Yan Hu, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
ISBN (Electronic)9781665429238
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2022-March
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/TerritoryIndia
CityKolkata
Period28/03/2231/03/22

Keywords

  • Retinal microsurgery
  • deep-learning
  • depth of field
  • focus measure
  • fusion

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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