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Histopathology in 3D: From three-dimensional reconstruction to multi-stain and multi-modal analysis.

Magee D, Song Y, Gilbert S, Roberts N, Wijayathunga N, Wilcox R, Bulpitt A, Treanor D - J Pathol Inform (2015)

Bottom Line: Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality.Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration.Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, University of Leeds, Leeds, UK ; HeteroGenius Limited, Leeds, UK.

ABSTRACT
Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

No MeSH data available.


Related in: MedlinePlus

Tissue class images: (a) Two histopathology images with different stains (left: Original images and sub image, right: 3 “tissue class probability images” corresponding to each image/tissue class, (b) histopathology image and magnetic resonance imaging image)
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Figure 3: Tissue class images: (a) Two histopathology images with different stains (left: Original images and sub image, right: 3 “tissue class probability images” corresponding to each image/tissue class, (b) histopathology image and magnetic resonance imaging image)

Mentions: Once the mapping functions for each image have been determined, the construction of probability images for each tissue class for each image is simply a matter of considering the co-occurrence of prototype labels in one image with tissue classes in the other. Counting these co-occurrences and normalizing gives P(Tissue Class/Prototype), which is mapped to a pixel value by multiplying by 255. Figure 3 illustrates results of applying this process for both multi-stain histopathology pairs and histopathology: Magnetic resonance imaging (MRI) pairs. Once the images have been constructed registration is applied as described in Section 2, with 5 x Nc vectors per block (where Nc is the number of tissue classes). Initial rigid alignment is using the same greyscale phase correlation method as described previously, which works on such multi-modal data (at low resolution) because of the clear distinction between foreground and background at low resolution in histopathology images. Full details may be found in Song et al.[11]


Histopathology in 3D: From three-dimensional reconstruction to multi-stain and multi-modal analysis.

Magee D, Song Y, Gilbert S, Roberts N, Wijayathunga N, Wilcox R, Bulpitt A, Treanor D - J Pathol Inform (2015)

Tissue class images: (a) Two histopathology images with different stains (left: Original images and sub image, right: 3 “tissue class probability images” corresponding to each image/tissue class, (b) histopathology image and magnetic resonance imaging image)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355830&req=5

Figure 3: Tissue class images: (a) Two histopathology images with different stains (left: Original images and sub image, right: 3 “tissue class probability images” corresponding to each image/tissue class, (b) histopathology image and magnetic resonance imaging image)
Mentions: Once the mapping functions for each image have been determined, the construction of probability images for each tissue class for each image is simply a matter of considering the co-occurrence of prototype labels in one image with tissue classes in the other. Counting these co-occurrences and normalizing gives P(Tissue Class/Prototype), which is mapped to a pixel value by multiplying by 255. Figure 3 illustrates results of applying this process for both multi-stain histopathology pairs and histopathology: Magnetic resonance imaging (MRI) pairs. Once the images have been constructed registration is applied as described in Section 2, with 5 x Nc vectors per block (where Nc is the number of tissue classes). Initial rigid alignment is using the same greyscale phase correlation method as described previously, which works on such multi-modal data (at low resolution) because of the clear distinction between foreground and background at low resolution in histopathology images. Full details may be found in Song et al.[11]

Bottom Line: Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality.Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration.Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, University of Leeds, Leeds, UK ; HeteroGenius Limited, Leeds, UK.

ABSTRACT
Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

No MeSH data available.


Related in: MedlinePlus