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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.

No MeSH data available.


Related in: MedlinePlus

Tissue types by manual spatial partitioning.Another (unsmoothed) canonical anti-pimonidazole image (top), its manual partitioning (middle), and its labeled partitions (bottom). Key: V = viable, N = necrotic; unlabeled, brown regions are hypoxic.
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pone.0153623.g004: Tissue types by manual spatial partitioning.Another (unsmoothed) canonical anti-pimonidazole image (top), its manual partitioning (middle), and its labeled partitions (bottom). Key: V = viable, N = necrotic; unlabeled, brown regions are hypoxic.

Mentions: Next we consider spatial partitioning, where continuous boundaries that separate tissue types are introduced into the image. This requires some degree of familiarity to manually parse these histology images, and so lacks the scalability in the number of images we require for statistical analysis. In Fig 4 we have another canonical anti-pimonidazole image, its manual partitioning, and its labeled partitions. Segmentation by partitioning reveals containment properties of the different regions and leads us to infer which tissue structures are nestable.


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

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

Tissue types by manual spatial partitioning.Another (unsmoothed) canonical anti-pimonidazole image (top), its manual partitioning (middle), and its labeled partitions (bottom). Key: V = viable, N = necrotic; unlabeled, brown regions are hypoxic.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g004: Tissue types by manual spatial partitioning.Another (unsmoothed) canonical anti-pimonidazole image (top), its manual partitioning (middle), and its labeled partitions (bottom). Key: V = viable, N = necrotic; unlabeled, brown regions are hypoxic.
Mentions: Next we consider spatial partitioning, where continuous boundaries that separate tissue types are introduced into the image. This requires some degree of familiarity to manually parse these histology images, and so lacks the scalability in the number of images we require for statistical analysis. In Fig 4 we have another canonical anti-pimonidazole image, its manual partitioning, and its labeled partitions. Segmentation by partitioning reveals containment properties of the different regions and leads us to infer which tissue structures are nestable.

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