Spectral imaging perspective on cytomics

Levenson, Richard M.

doi:10.1002/cyto.a.20292

Cited by 55 publications

(42 citation statements)

References 34 publications

(35 reference statements)

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“…However, the data generated by this new imaging technology are not limited to pixel-level registration; with additional analysis and co-registration of other stains the data can be used to generate information at a cellular and subcellular level to provide true quantitative histocytometric information (Levenson 2006;Levenson and Mansfield 2006).…”

Section: Combining Small Animal Imaging and Microscopy (Ii)mentioning

confidence: 99%

Multiplexing with Multispectral Imaging: From Mice to Microscopy

Levenson

Lynch²,

Kobayashi

et al. 2008

ILAR Journal

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Increasing sophistication in the design and application of biological models as well as the advent of novel fluorescent probes have led to new demands on molecular imaging systems to deliver enhanced sensitivity, reliable quantitation, and the ability to resolve multiple simultaneous signals. Sensitivity is limited, especially in the visible spectral range, by the presence of ubiquitous autofluorescence signals (mostly arising from the skin and gut), which need to be separated from those of targeted fluorophores. Fluorescence-based imaging is also affected by absorbing and scattering properties of tissue in both the visible and to a lesser extent the near-infrared (NIR) regions. However, the small size of typical animal models (usually mice) often permits the detection of enough light arising even from relatively deep locations to allow the capture of signals with an acceptable signal-to-noise ratio. Multispectral imaging, through its ability to separate autofluorescence from label fluorescence, can increase sensitivity as much as 300 times compared to conventional approaches, and concomitantly improve quantitative accuracy. In the NIR region, autofluorescence, while still significant, poses less of a problem. However, the task of disentangling signals from multiple fluorophores remains. Multispectral imaging allows the separation of five or more fluorophores, with each signal quantitated and visualized separately. Preclinical small animal imaging is often accompanied by microscopic analysis, both before and after the in vivo phase. This can involve tissue culture manipulations and/or histological examination of fixed or frozen tissue. Due to the same advantages in sensitivity, quantitation, and multiplexing, microscopy-based multispectral techniques form an excellent complement to in vivo imaging.

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Section: Combining Small Animal Imaging and Microscopy (Ii)mentioning

confidence: 99%

Multiplexing with Multispectral Imaging: From Mice to Microscopy

Levenson

Lynch²,

Kobayashi

et al. 2008

ILAR Journal

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show abstract

“…Typical variabilities in the intensity distribution across cells are attributed to uneven staining, out-offocal-plane cells due to layering, and inter-cellular structures, each of which makes the segmentation task even more difficult. More recently, the use of spectral microscopy for imaging cytological smears has made it possible to capture images with accurate spectral content correlated with spatial information, thereby revealing the chemical or anatomic features of the cells [3]. In this paper, we propose a segmentation method that leverages a local appearance model built on high dimensional spectral data and the pair-wise spatial constraint modeled by the Di Zenzo gradient of a multichannel-image [4].…”

Section: Introductionmentioning

confidence: 99%

Cell segmentation in multispectral images using level sets with priors for accurate shape recovery

Shah

2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In this paper, we demonstrate the effectiveness of using statistical shape priors to recover shape descriptors from occluded objects in a level set based variational framework. Parameters that balance curve evolution forces are estimated systematically through embedded discrete Conditional Random Field (CRF). In addition, our approach exploits the benefit of using spectral data to construct a local appearance model for images with intensity inhomogeneity. The proposed segmentation approach is evaluated on cytological smears imaged using spectral microscopy and compared against traditional cell segmentation algorithms.

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“…Spectrally resolved optical imaging of whole animals is attractive because it offers a low-cost, non-invasive imaging method compared with magnetic resonance (MR) imaging or positron emission tomography (PET) (1)(2)(3). Using spectral unmixing algorithms, the background autofluorescence signals can be separated from the target signals generated from endogenous or exogenous fluorophores (1)(2)(3), resulting in high targetto-background ratios.…”

Section: Introductionmentioning

confidence: 99%

“…Using spectral unmixing algorithms, the background autofluorescence signals can be separated from the target signals generated from endogenous or exogenous fluorophores (1)(2)(3), resulting in high targetto-background ratios. This technology has become a powerful tool for in vivo cancer imaging research (2)(3)(4)(5). However, one of the major limitations is that target structures are obscured by background autofluorescence and blurred by light scattering and defocusing within the volume of tissue being imaged.…”

Section: Introductionmentioning

confidence: 99%

Spectral near‐infrared fluorescence imaging of curved surfaces using projection reconstruction algorithms

Hama

Koyama

Bernardo

et al. 2007

Contrast Media & Molecular

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In vivo spectral fluorescence imaging has made it possible to non-invasively visualize superficial curved structures as well as structures deep to the skin. However, the defocus created by blurring has been an obstacle to creating anatomically interpretable surface images. Herein we present a methodology to correct for blurring induced by curved structures during spectral fluorescence imaging using signal intensity projection algorithms. In a phantom and an animal model in which the lymphatic system was visualized after the interstitial injection of quantum dots with emission spectra in the near-infrared (NIR) range, the planes of focus were sequentially adjusted to obtain a z-stack of images which contains images acquired from multiple focal points. Maximum, minimum, median and average intensity projections were applied to the resulting images. Using the phantom, the minimum and the median intensity projection images demonstrated improved deblurring whereas during in vivo imaging the median intensity projection images more clearly visualized important structures than did the other projection techniques. Image stacking with subsequent application of appropriate projection techniques provides a simple method for deblurring in vivo optical images obtained from curved surfaces, thus improving their anatomic resolution.

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Spectral imaging perspective on cytomics

Cited by 55 publications

References 34 publications

Multiplexing with Multispectral Imaging: From Mice to Microscopy

Multiplexing with Multispectral Imaging: From Mice to Microscopy

Cell segmentation in multispectral images using level sets with priors for accurate shape recovery

Spectral near‐infrared fluorescence imaging of curved surfaces using projection reconstruction algorithms

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