Denoisereg: Unsupervised Joint Denoising and Registration of Time-Lapse Live Cell Microscopy Images Using Deep Learning

Kerem Celikay, Vadim O. Chagin, M. C. Cardoso, K. Rohr
{"title":"Denoisereg: Unsupervised Joint Denoising and Registration of Time-Lapse Live Cell Microscopy Images Using Deep Learning","authors":"Kerem Celikay, Vadim O. Chagin, M. C. Cardoso, K. Rohr","doi":"10.1109/ISBI52829.2022.9761507","DOIUrl":null,"url":null,"abstract":"Image registration is important for analysing time-lapse live cell microscopy images. However, this is challenging due to significant image noise and complex cell movement. We propose a novel end-to-end trainable deep neural network for joint denoising and affine registration of temporal live cell microscopy images. Our network is trained unsupervised, and only a single network is required for both tasks which reduces overfitting. Our experiments show that the proposed network performs better than deep affine registration without denoising, and better than sequential deep denoising and affine registration. In combination with deep non-rigid registration, we outperform state-of-the-art non-rigid registration methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"114 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI52829.2022.9761507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Image registration is important for analysing time-lapse live cell microscopy images. However, this is challenging due to significant image noise and complex cell movement. We propose a novel end-to-end trainable deep neural network for joint denoising and affine registration of temporal live cell microscopy images. Our network is trained unsupervised, and only a single network is required for both tasks which reduces overfitting. Our experiments show that the proposed network performs better than deep affine registration without denoising, and better than sequential deep denoising and affine registration. In combination with deep non-rigid registration, we outperform state-of-the-art non-rigid registration methods.
Denoisereg:使用深度学习的延时活细胞显微镜图像的无监督联合去噪和配准
图像配准是分析延时活细胞显微镜图像的重要方法。然而,由于明显的图像噪声和复杂的细胞运动,这是具有挑战性的。我们提出了一种新的端到端可训练的深度神经网络,用于时间活细胞显微镜图像的联合去噪和仿射配准。我们的网络是无监督训练的,两个任务只需要一个网络,这减少了过拟合。实验表明,该方法优于不去噪的深度仿射配准,也优于连续深度去噪和仿射配准。结合深度非刚性配准,我们优于最先进的非刚性配准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信