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Mesoscopic Fluorescence Tomography for In-vivo Imaging of Developing Drosophila

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

¹Beijing QED Technique Co., Ltd., Beijing, China.

Journal of Medical Imaging (Bellingham, Wash.)

|March 7, 2019

Summary

A new deep learning algorithm improves fluorescence molecular tomography (FMT) reconstruction. This method enhances image clarity and accuracy for molecular imaging, especially in challenging scattering media.

Keywords:

deep learning image reconstruction in silico experiments mesoscopic fluorescence molecular tomography

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Area of Science:

Biomedical Imaging
Optical Imaging
Molecular Imaging

Background:

Fluorescence molecular tomography (FMT) and mesoscopic FMT (MFMT) are crucial for studying molecular processes in vivo and ex vivo.
Challenges in FMT/MFMT include achieving depth-localized and sharp reconstructions, particularly in highly scattering tissues.

Purpose of the Study:

To develop a novel two-stage deep learning algorithm for improved 3-D reconstruction in MFMT.
To address the limitations of current methods in handling scattering media and achieving precise localization.

Main Methods:

A two-stage 3-D convolutional neural network (CNN) based reconstruction algorithm was proposed.
The CNN was designed to accurately predict reconstruction boundaries for enhanced image refinement.

Main Results:

In silico experiments demonstrated significant improvements over conventional algorithms.
The deep learning approach reduced relative volume and absolute centroid errors while increasing intersection over union by over 15%.

Conclusions:

The proposed deep learning-based method shows significant potential for advancing MFMT reconstruction.
Machine learning, specifically deep learning, offers a promising future for improving molecular imaging techniques.