Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration

Sehyung Lee, Makiko Negishi, Hidetoshi Urakubo, Haruo Kasai, Shin Ishii

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)

Abstract

Advances in two two-photon microscopy (2PM) have made three-dimensional (3D) neural imaging of deep cortical regions possible. However, 2PM often suffers from poor image quality because of various noise factors, including blur, white noise, and photo bleaching. In addition, the effectiveness of the existing image processing methods is limited because of the special features of 2PM images such as deeper tissue penetration but higher image noises owing to rapid laser scanning. To address the denoising problems in 2PM 3D images, we present a new algorithm based on deep convolutional neural networks (CNNs). The proposed model consists of multiple U-nets in which an individual U-net removes noises at different scales and then yields a performance improvement based on a coarse-to-fine strategy. Moreover, the constituent CNNs employ fully 3D convolution operations. Such an architecture enables the proposed model to facilitate end-to-end learning without any pre/post processing. Based on the experiments on 2PM image denoising, we observed that our new algorithm demonstrates substantial performance improvements over other baseline methods.

Original languageEnglish
Pages (from-to)92-103
Number of pages12
JournalNeural Networks
Volume125
DOIs
Publication statusPublished - 05-2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

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