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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

Otsu segmentation and smoothing.How Otsu’s multithreshold segmentation differs between unsmoothed gray (upper left) and smoothed gray (lower left) images. Corresponding images on the right show dark blue regions that denote hypoxic cells, light blue regions that denote viable cells, and yellow regions that denote necrotic cells.
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pone.0153623.g005: Otsu segmentation and smoothing.How Otsu’s multithreshold segmentation differs between unsmoothed gray (upper left) and smoothed gray (lower left) images. Corresponding images on the right show dark blue regions that denote hypoxic cells, light blue regions that denote viable cells, and yellow regions that denote necrotic cells.

Mentions: We applied Otsu’s method to multithreshold a set of nT = 66 images across stratification criteria, magnification, and high and low concentrations of anti-pimonidazole. To distinguish between results for the high- and low-concentration images, we place, alongside the results for the total set of images, those results for nH = 36 high-concentration images and nL = 30 low-concentration images, computed separately. See Table 1. The table organization also reflects the distinction between unsmoothed and smoothed gray images. We illustrate this distinction in Fig 5.


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

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

Otsu segmentation and smoothing.How Otsu’s multithreshold segmentation differs between unsmoothed gray (upper left) and smoothed gray (lower left) images. Corresponding images on the right show dark blue regions that denote hypoxic cells, light blue regions that denote viable cells, and yellow regions that denote necrotic cells.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g005: Otsu segmentation and smoothing.How Otsu’s multithreshold segmentation differs between unsmoothed gray (upper left) and smoothed gray (lower left) images. Corresponding images on the right show dark blue regions that denote hypoxic cells, light blue regions that denote viable cells, and yellow regions that denote necrotic cells.
Mentions: We applied Otsu’s method to multithreshold a set of nT = 66 images across stratification criteria, magnification, and high and low concentrations of anti-pimonidazole. To distinguish between results for the high- and low-concentration images, we place, alongside the results for the total set of images, those results for nH = 36 high-concentration images and nL = 30 low-concentration images, computed separately. See Table 1. The table organization also reflects the distinction between unsmoothed and smoothed gray images. We illustrate this distinction in Fig 5.

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