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Project

Light Field Imaging Plugin


Award napari Plugin Accelerator

Project Summary

This project builds on existing Python code for processing light field images, typically recorded on a microscope. Lfanalyze handles light field images of fluorescently labeled, transparent specimens or similar objects with optical properties characterized by a single parameter associated with voxels in object space (e.g. fluorescence density or absorption coefficient). The team will port the code from Python 2.7 to Python 3.9 and adapt it as a plugin to napari, providing functions that generate optical sections of an object and projections of it along different viewing directions. In addition to the port, the group will enhance the code by including functions for processing light field images using deep learning and other neural network approaches, such as LFMNet. LFMNet is an existing Python framework for learning to reconstruct confocal microscope stacks from single light field images. LFMNet was developed by Josue Page, who is now at the Technical University in Munich and who agreed to support the inclusion of the LFMNet framework in napari plugins for light field imaging. As a further benefit of this inclusion, this project will refine the interface between napari and the HDF5 data file format.

Investigators

Principal Investigator
Rudolf Oldenbourg, MA, PhD
Rudolf Oldenbourg, MA, PhD
Co-Principal Investigators
Patrick La Riviere, PhD
Patrick La Riviere, PhD