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Quantitative analysis of myocardial tissue with digital autofluorescence microscopy.

Jensen T, Holten-Rossing H, Svendsen IM, Jacobsen C, Vainer B - J Pathol Inform (2016)

Bottom Line: This data may provide a basic histological starting point from which further digital analysis including staining may benefit.The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue.The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

ABSTRACT

Background: The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit.

Methods: This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented.

Results: It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining.

Conclusion: The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

No MeSH data available.


Related in: MedlinePlus

Transition analysis plot of drop in the ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (Δn0,02/nstarting point - 0,40) plotted against the intensities of the analyzed range. The red line is an exponential fit asymptotically approaching zero
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Figure 3: Transition analysis plot of drop in the ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (Δn0,02/nstarting point - 0,40) plotted against the intensities of the analyzed range. The red line is an exponential fit asymptotically approaching zero

Mentions: Step 4: Creation of a mask of the fibrous tissue by generating and analyzing the blue-to-red ratio image. In summary, the standard deviation (SD) of the myocyte peak in the ratio image is used to approximate the upper-intensity level that includes pixels covering myocytes, and a transition analysis is performed to reproducibly identify that particular level. In the case of the blue to red ratio image, fibrous tissue is considered all pixels with intensity above the upper intensity level of the myocytes, and in the case of the red-to-blue ratio image, lipofuscin is considered all pixels with intensity above the level of the myocytes. The detailed procedure for identifying fibrous tissue is described in the following: The blue image is divided with the red image using the image calculator function; 32-bit float must be checked. In the resulting ratio image, the intensity variation of all tissue elements is leveled, and the fibrous tissue intensity is amplified, and lipofuscin intensity is diminished. Typically, fibrous tissue is too small a representation to be represented by a peak in the intensity histogram of the generated ratio image [Figure 1c]. Hence, fibrous tissue must be defined based on the myocyte peak as follows: A standard distribution is fitted to the myocyte peak of the ratio image, which in theory is located at 1.0. By eye observation, it was established that the transition from myocytes to fibrous tissue is approximately at 5 SD above the myocyte peak intensity, where intensity values above represent fibrous tissue and intensities below represent myocytes. Thus, peak intensity + 5 SD is used as the starting point of the myocyte-to-fibrous tissue transition analysis. The transition analysis considers the drop in pixels covering myocytes as the threshold approaches the signal emanating from fibrous tissue. In particular, single pixel presentations are regarded as reminiscences of myocytes. All single-pixel representations above threshold are quantitated for each 0.02 threshold interval in the range: Starting point (myocyte peak intensity + 5 SD) ± 0.40, which covers approximately 10% of the intensity range of the generated 32-bit ratio image. The ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (∆n0.02 /nstartingpoint - 0.40) is plotted against the intensities of the analyzed range. It is noted that the change in single pixel presentations approach zero with increasing threshold [Figure 3]. In the plots, an exponential equation approaching zero asymptotically is fitted using the function “fit exponential → asymptotic1” in origin [Figure 3]. The adjusted R2 of the 20 fits performed in this study was typically around 0.95 and 0.88 at lowest. The fit provides the basis for selecting the threshold reproducibly, which was defined in this study as the intensity of the fit corresponding to the value ∆n/nstartingpoint - 0.40 = −0.015


Quantitative analysis of myocardial tissue with digital autofluorescence microscopy.

Jensen T, Holten-Rossing H, Svendsen IM, Jacobsen C, Vainer B - J Pathol Inform (2016)

Transition analysis plot of drop in the ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (Δn0,02/nstarting point - 0,40) plotted against the intensities of the analyzed range. The red line is an exponential fit asymptotically approaching zero
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837794&req=5

Figure 3: Transition analysis plot of drop in the ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (Δn0,02/nstarting point - 0,40) plotted against the intensities of the analyzed range. The red line is an exponential fit asymptotically approaching zero
Mentions: Step 4: Creation of a mask of the fibrous tissue by generating and analyzing the blue-to-red ratio image. In summary, the standard deviation (SD) of the myocyte peak in the ratio image is used to approximate the upper-intensity level that includes pixels covering myocytes, and a transition analysis is performed to reproducibly identify that particular level. In the case of the blue to red ratio image, fibrous tissue is considered all pixels with intensity above the upper intensity level of the myocytes, and in the case of the red-to-blue ratio image, lipofuscin is considered all pixels with intensity above the level of the myocytes. The detailed procedure for identifying fibrous tissue is described in the following: The blue image is divided with the red image using the image calculator function; 32-bit float must be checked. In the resulting ratio image, the intensity variation of all tissue elements is leveled, and the fibrous tissue intensity is amplified, and lipofuscin intensity is diminished. Typically, fibrous tissue is too small a representation to be represented by a peak in the intensity histogram of the generated ratio image [Figure 1c]. Hence, fibrous tissue must be defined based on the myocyte peak as follows: A standard distribution is fitted to the myocyte peak of the ratio image, which in theory is located at 1.0. By eye observation, it was established that the transition from myocytes to fibrous tissue is approximately at 5 SD above the myocyte peak intensity, where intensity values above represent fibrous tissue and intensities below represent myocytes. Thus, peak intensity + 5 SD is used as the starting point of the myocyte-to-fibrous tissue transition analysis. The transition analysis considers the drop in pixels covering myocytes as the threshold approaches the signal emanating from fibrous tissue. In particular, single pixel presentations are regarded as reminiscences of myocytes. All single-pixel representations above threshold are quantitated for each 0.02 threshold interval in the range: Starting point (myocyte peak intensity + 5 SD) ± 0.40, which covers approximately 10% of the intensity range of the generated 32-bit ratio image. The ratio of change in single pixel presentations to the number of single pixel presentation at the initial point of the analysis (∆n0.02 /nstartingpoint - 0.40) is plotted against the intensities of the analyzed range. It is noted that the change in single pixel presentations approach zero with increasing threshold [Figure 3]. In the plots, an exponential equation approaching zero asymptotically is fitted using the function “fit exponential → asymptotic1” in origin [Figure 3]. The adjusted R2 of the 20 fits performed in this study was typically around 0.95 and 0.88 at lowest. The fit provides the basis for selecting the threshold reproducibly, which was defined in this study as the intensity of the fit corresponding to the value ∆n/nstartingpoint - 0.40 = −0.015

Bottom Line: This data may provide a basic histological starting point from which further digital analysis including staining may benefit.The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue.The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

ABSTRACT

Background: The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit.

Methods: This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented.

Results: It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining.

Conclusion: The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

No MeSH data available.


Related in: MedlinePlus