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Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia.

Sundstrom A, Grabocka E, Bar-Sagi D, Mishra B - PLoS ONE (2016)

Bottom Line: We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia.Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions.From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.

ABSTRACT
Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.

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Synthetic histology.Inferred hypoxia gradients (gray) superimposed onto the canonical raw H&E (top) and anti-pimonidazole (bottom) images at half-opacity. Note that the positions of the gradient centers have been corrected as per our earlier observation regarding adjacent image registration (see S7 Fig).
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pone.0153623.g006: Synthetic histology.Inferred hypoxia gradients (gray) superimposed onto the canonical raw H&E (top) and anti-pimonidazole (bottom) images at half-opacity. Note that the positions of the gradient centers have been corrected as per our earlier observation regarding adjacent image registration (see S7 Fig).

Mentions: Even with these limitations, one can create synthetic images by superimposing measured gradients on the original raw images. We illustrate this as follows. Using the segmented least-squares fits to the gradient functions measured in S1 Fig (bottom) and presented in Fig 3, we superimpose the corresponding gradients upon the raw anti-pimonidazole and H&E images (see Fig 6). Here, concentrically-plotted gray levels mirror the respective measured gradient values. The latter half-opacity (α = 0.5) synthesized image nondestructively combines the inferred information from the anti-pimonidazole image and the high-contrast structural information from the H&E image into a single view. These synthetic images could serve as a diagnostic tool in a clinical setting.


Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia.

Sundstrom A, Grabocka E, Bar-Sagi D, Mishra B - PLoS ONE (2016)

Synthetic histology.Inferred hypoxia gradients (gray) superimposed onto the canonical raw H&E (top) and anti-pimonidazole (bottom) images at half-opacity. Note that the positions of the gradient centers have been corrected as per our earlier observation regarding adjacent image registration (see S7 Fig).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g006: Synthetic histology.Inferred hypoxia gradients (gray) superimposed onto the canonical raw H&E (top) and anti-pimonidazole (bottom) images at half-opacity. Note that the positions of the gradient centers have been corrected as per our earlier observation regarding adjacent image registration (see S7 Fig).
Mentions: Even with these limitations, one can create synthetic images by superimposing measured gradients on the original raw images. We illustrate this as follows. Using the segmented least-squares fits to the gradient functions measured in S1 Fig (bottom) and presented in Fig 3, we superimpose the corresponding gradients upon the raw anti-pimonidazole and H&E images (see Fig 6). Here, concentrically-plotted gray levels mirror the respective measured gradient values. The latter half-opacity (α = 0.5) synthesized image nondestructively combines the inferred information from the anti-pimonidazole image and the high-contrast structural information from the H&E image into a single view. These synthetic images could serve as a diagnostic tool in a clinical setting.

Bottom Line: We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia.Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions.From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.

ABSTRACT
Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.

No MeSH data available.


Related in: MedlinePlus