Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study

Feixiao Long

doi:10.1117/1.JMI.5.3.036001

4 September 2018 Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study

Feixiao Long

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 3, 036001 (September 2018). https://doi.org/10.1117/1.JMI.5.3.036001

Abstract

Fluorescence molecular tomography (FMT), as well as mesoscopic FMT (MFMT) is widely employed to investigate molecular level processes ex vivo or in vivo. However, acquiring depth-localized and less blurry reconstruction still remains challenging, especially when fluorophore (dye) is located within large scattering coefficient media. Herein, a two-stage deep learning-based three-dimensional (3-D) reconstruction algorithm is proposed. The key point for the proposed algorithm is to employ a 3-D convolutional neural network to correctly predict the boundary of reconstructions, leading refined results. Compared with conventional algorithm, in silico experiments show that relative volume and absolute centroid error reduce over ∼50 % whereas intersection over union increases over 15% for most situations. These results preliminarily indicate the promising future of appropriately applying machine learning (deep learning)-based methods in MFMT.

Citation Download Citation

Feixiao Long "Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study," Journal of Medical Imaging 5(3), 036001 (4 September 2018). https://doi.org/10.1117/1.JMI.5.3.036001

Received: 4 April 2018; Accepted: 8 August 2018; Published: 4 September 2018

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