Dataset Information

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

ABSTRACT: Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context.The proposed method was effective at improving spectral unmixing performance by exploiting: (i) a set of dictionary-based models for object morphologies learned from the image data; and (ii) models of spatial context learned from the image data using a total variation algorithm. The method was evaluated on multi-spectral images of multiplex-labeled pancreatic ductal adenocarcinoma (PDAC) tissue samples. The former constraint ensures that neighbouring pixels correspond to morphologically similar structures, and the latter constraint ensures that neighbouring pixels have similar spectral signatures. The average Mean Squared Error (MSE) and Signal Reconstruction Error (SRE) ratio of the proposed method was 39.6% less and 8% more, respectively, compared to the best of all other algorithms that do not exploit these spatial constraints.Open source software (MATLAB).broysam@central.uh.edu.Supplementary data are available at Bioinformatics online.

SUBMITTER: Megjhani M

PROVIDER: S-EPMC5951100 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

Megjhani Murad M Correa de Sampaio Pedro P Leigh Carstens Julienne J Kalluri Raghu R Roysam Badrinath B

Bioinformatics (Oxford, England) 20170701 14

<h4>Motivation</h4>Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context.<h4>Results</h4>The proposed method was effective at improvi ...[more]

PMID: 28334208

Similar Datasets

Project description:BackgroundLiving tissues contain a range of intrinsic fluorophores and sources of second harmonic generation which provide contrast that can be exploited for fresh tissue imaging. Microscopic imaging of fresh tissue samples can circumvent the cost and time associated with conventional histology. Further, intrinsic contrast can provide rich information about a tissue's composition, structure and function, and opens the potential for in-vivo imaging without the need for contrast agents.Methodology/principal findingsIn this study, we used hyperspectral two-photon microscopy to explore the characteristics of both normal and diseased gastrointestinal (GI) tissues, relying only on their endogenous fluorescence and second harmonic generation to provide contrast. We obtained hyperspectral data at subcellular resolution by acquiring images over a range of two-photon excitation wavelengths, and found excitation spectral signatures of specific tissue types based on our ability to clearly visualize morphology. We present the two-photon excitation spectral properties of four major tissue types that are present throughout the GI tract: epithelium, lamina propria, collagen, and lymphatic tissue. Using these four excitation signatures as basis spectra, linear unmixing strategies were applied to hyperspectral data sets of both normal and neoplastic tissue acquired in the colon and small intestine. Our results show that hyperspectral unmixing with excitation spectra allows segmentation, showing promise for blind identification of tissue types within a field of view, analogous to specific staining in conventional histology. The intrinsic spectral signatures of these tissue types provide information relating to their biochemical composition.Conclusions/significanceThese results suggest hyperspectral two-photon microscopy could provide an alternative to conventional histology either for in-situ imaging, or intraoperative 'instant histology' of fresh tissue biopsies.

Dataset Information

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

Publications

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets