Super-resolution single-molecule microscopic reconstruction based on improved deep-STORM

Qinfang Zhang; Lu Lou; Qihuan Li; Zhengxia Wang

doi:10.1117/12.2656547

20 October 2022 Super-resolution single-molecule microscopic reconstruction based on improved deep-STORM

Qinfang Zhang, Lu Lou, Qihuan Li, Zhengxia Wang

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511Y (2022) https://doi.org/10.1117/12.2656547
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

With the development of fluorescence super-resolution microscopic imaging technology, super-resolution optical microscopic imaging technology, which breaks through optical diffraction, has rapidly developed into one of the powerful research technologies in the field of life sciences due to its nanometer-level spatial resolution and low damage to samples. Since 2016, deep learning-based methods have been proposed for reconstructing super-resolution microscopy images. In this paper, we make two improvements based on the Deep-STORM network, first replacing the original full convolution with the dilated convolution. Then, the skip connection was added on top of the dilated convolution to reconstruct the microtubule dataset of the EPFL·SMLM challenge site. The experimental results show that NMSEs are reduced by 36.84% and 37.99% respectively on the two improved methods compared to CEL0. Compared to FALCON, NMSEs are reduced by 24.69% and 25.84% respectively. Compared to Deep-STORM, the NMSEs are reduced by 2.2% and 3.35% respectively. Using this method, super-resolution microscopic images at the nanometer-level can be reconstructed quickly and accurately, thus meeting the requirement to observe subcellular microtubule structures beyond the diffraction limit.

Citation Download Citation

Qinfang Zhang, Lu Lou, Qihuan Li, and Zhengxia Wang "Super-resolution single-molecule microscopic reconstruction based on improved deep-STORM", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511Y (20 October 2022); https://doi.org/10.1117/12.2656547

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KEYWORDS

Super resolution

Computer simulations

Convolution

Microscopy

Network architectures

Reconstruction algorithms

Diffraction

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