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Real-time high dynamic range laser scanning microscopy.

Vinegoni C, Leon Swisher C, Fumene Feruglio P, Giedt RJ, Rousso DL, Stapleton S, Weissleder R - Nat Commun (2016)

Bottom Line: In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes.We address reconstruction and segmentation performance on samples with different size, anatomy and complexity.Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA.

ABSTRACT
In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

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Cell body segmentation.HDR imaging allows for accurate quantification of cell bodies in sparsely populated fixed tissue specimens. LDRs (a,b) and rHDR (c) images of the neural cells shown in Fig. 5d (region α). (d–f) The cell bodies were segmented for each LDR and HDR image using a trainable Weka algorithm (see ‘Methods' section). In each image is indicated the number of body cells identified by the automatic segmentation algorithm. (g–i) Magnified image of the box area shown in d. Colours are used to help to visualize and distinguish among the different cell bodies present within the field of view. (j) Comparison of segmentation performance (defined as total number of cell bodies detected) between LDRs and HDR images over four different images areas. Manual segmentation and counting is used to establish the ground truth. The barplots demonstrate the improved performance of the automatic segmentation algorithm when applied to the HDR images, compared with LDR image, versus manual segmentation and counting. Image colour bar: red saturation levels. Scale bars, 100 μm. Fluo., fluorescence.
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f8: Cell body segmentation.HDR imaging allows for accurate quantification of cell bodies in sparsely populated fixed tissue specimens. LDRs (a,b) and rHDR (c) images of the neural cells shown in Fig. 5d (region α). (d–f) The cell bodies were segmented for each LDR and HDR image using a trainable Weka algorithm (see ‘Methods' section). In each image is indicated the number of body cells identified by the automatic segmentation algorithm. (g–i) Magnified image of the box area shown in d. Colours are used to help to visualize and distinguish among the different cell bodies present within the field of view. (j) Comparison of segmentation performance (defined as total number of cell bodies detected) between LDRs and HDR images over four different images areas. Manual segmentation and counting is used to establish the ground truth. The barplots demonstrate the improved performance of the automatic segmentation algorithm when applied to the HDR images, compared with LDR image, versus manual segmentation and counting. Image colour bar: red saturation levels. Scale bars, 100 μm. Fluo., fluorescence.

Mentions: We also used a trainable Weka (Waikato Environment for Knowledge Analysis) segmentation algorithm (see ‘Methods' section), which has been demonstrated in a range of imaging pipelines for many different imaging modalities, including two-photon microscopy. The results of the segmentation approach, including segmentation of cell bodies across different regions of the brain presenting distinct degrees of cell densities, is shown in Fig. 8 and Supplementary Fig. 19. To determine the improvement in performance of the segmentation approach across the different images, a direct comparison was made between automatic and manual (here used as a reference) segmentation approaches applied to both the LDR and HDR images. Higher accuracy was achieved using the automated segmentation algorithm when applied to the HDR images rather than the LDR images (Fig. 8). Specificity, sensitivity and accuracy of cell detection were computed based on the number of false positives (that are incorrectly classified as cell bodies), false negatives (that are undetected cells) and the total number of cell bodies (Supplementary Fig. 19).


Real-time high dynamic range laser scanning microscopy.

Vinegoni C, Leon Swisher C, Fumene Feruglio P, Giedt RJ, Rousso DL, Stapleton S, Weissleder R - Nat Commun (2016)

Cell body segmentation.HDR imaging allows for accurate quantification of cell bodies in sparsely populated fixed tissue specimens. LDRs (a,b) and rHDR (c) images of the neural cells shown in Fig. 5d (region α). (d–f) The cell bodies were segmented for each LDR and HDR image using a trainable Weka algorithm (see ‘Methods' section). In each image is indicated the number of body cells identified by the automatic segmentation algorithm. (g–i) Magnified image of the box area shown in d. Colours are used to help to visualize and distinguish among the different cell bodies present within the field of view. (j) Comparison of segmentation performance (defined as total number of cell bodies detected) between LDRs and HDR images over four different images areas. Manual segmentation and counting is used to establish the ground truth. The barplots demonstrate the improved performance of the automatic segmentation algorithm when applied to the HDR images, compared with LDR image, versus manual segmentation and counting. Image colour bar: red saturation levels. Scale bars, 100 μm. Fluo., fluorescence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4821995&req=5

f8: Cell body segmentation.HDR imaging allows for accurate quantification of cell bodies in sparsely populated fixed tissue specimens. LDRs (a,b) and rHDR (c) images of the neural cells shown in Fig. 5d (region α). (d–f) The cell bodies were segmented for each LDR and HDR image using a trainable Weka algorithm (see ‘Methods' section). In each image is indicated the number of body cells identified by the automatic segmentation algorithm. (g–i) Magnified image of the box area shown in d. Colours are used to help to visualize and distinguish among the different cell bodies present within the field of view. (j) Comparison of segmentation performance (defined as total number of cell bodies detected) between LDRs and HDR images over four different images areas. Manual segmentation and counting is used to establish the ground truth. The barplots demonstrate the improved performance of the automatic segmentation algorithm when applied to the HDR images, compared with LDR image, versus manual segmentation and counting. Image colour bar: red saturation levels. Scale bars, 100 μm. Fluo., fluorescence.
Mentions: We also used a trainable Weka (Waikato Environment for Knowledge Analysis) segmentation algorithm (see ‘Methods' section), which has been demonstrated in a range of imaging pipelines for many different imaging modalities, including two-photon microscopy. The results of the segmentation approach, including segmentation of cell bodies across different regions of the brain presenting distinct degrees of cell densities, is shown in Fig. 8 and Supplementary Fig. 19. To determine the improvement in performance of the segmentation approach across the different images, a direct comparison was made between automatic and manual (here used as a reference) segmentation approaches applied to both the LDR and HDR images. Higher accuracy was achieved using the automated segmentation algorithm when applied to the HDR images rather than the LDR images (Fig. 8). Specificity, sensitivity and accuracy of cell detection were computed based on the number of false positives (that are incorrectly classified as cell bodies), false negatives (that are undetected cells) and the total number of cell bodies (Supplementary Fig. 19).

Bottom Line: In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes.We address reconstruction and segmentation performance on samples with different size, anatomy and complexity.Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA.

ABSTRACT
In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

Show MeSH