2017 Winter Project Week/HyperspectralOpht

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  Projects List

Key Investigators

• Sungmin Hong (New York University)
• Guido Gerig (New York University)

Project Description

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis. 3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution. The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

Objective	Approach and Plan	Progress and Next Steps
3D/4D Ophthalmology Image Anaylsis Framework Read 4D hyperspectral data Viewer and interactor for 3D hi-res image data and 4D hyperspectral data Co-registration between 3D hi-res data and 4D hyperspectral data Cell segmentation in 3D hi-res data Statistics of cells (possibly location, size, distribution) Plot of spectra of selected cells or a region-of-interest	Review existing modules in 3D Slicer Review existing modules for 4D data viewer, such as, multi-volume viewer extension Try existing segmentation modules in Slicer to see if they can work on SIM data Review existing modules for cell statistics after segmentation Implementation/Integration Implement/integrate 4D hyperspectral data viewer to show image and spectral information. Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module Implement a viewer and an interactor for cell statistics User Manual Create an user manual to comprehend a overview of an extension Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.