Limits...
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

Loci of single-bundle hypoxia gradients.Circles (red) defined by the rm found by the Intensity-Sample-Ray-Bundles algorithm for each of the three centers we specified, corresponding to vessel locations in the registered H&E image. Here we show m = 1 sector (2π radians per sector) for each center. Sectors are labeled with red numbers, counterclockwise, just outside of the red sector contour.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g002: Loci of single-bundle hypoxia gradients.Circles (red) defined by the rm found by the Intensity-Sample-Ray-Bundles algorithm for each of the three centers we specified, corresponding to vessel locations in the registered H&E image. Here we show m = 1 sector (2π radians per sector) for each center. Sectors are labeled with red numbers, counterclockwise, just outside of the red sector contour.

Mentions: Fig 2 shows the circles (red) defined by the rm found for each of the three centers specified in our canonical image (n = 80, m = 1), corresponding to vessel locations in the registered H&E image. The intensity analysis for the three circles’ areas is given in Fig 3. S6 Fig shows the sectors (red) defined by the rm found for each bundle of each of the three centers specified in our canonical image (n = 80, m = 8), corresponding to vessel locations in the registered H&E image. We do not show the corresponding 24 intensity analysis figures.


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

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

Loci of single-bundle hypoxia gradients.Circles (red) defined by the rm found by the Intensity-Sample-Ray-Bundles algorithm for each of the three centers we specified, corresponding to vessel locations in the registered H&E image. Here we show m = 1 sector (2π radians per sector) for each center. Sectors are labeled with red numbers, counterclockwise, just outside of the red sector contour.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153623.g002: Loci of single-bundle hypoxia gradients.Circles (red) defined by the rm found by the Intensity-Sample-Ray-Bundles algorithm for each of the three centers we specified, corresponding to vessel locations in the registered H&E image. Here we show m = 1 sector (2π radians per sector) for each center. Sectors are labeled with red numbers, counterclockwise, just outside of the red sector contour.
Mentions: Fig 2 shows the circles (red) defined by the rm found for each of the three centers specified in our canonical image (n = 80, m = 1), corresponding to vessel locations in the registered H&E image. The intensity analysis for the three circles’ areas is given in Fig 3. S6 Fig shows the sectors (red) defined by the rm found for each bundle of each of the three centers specified in our canonical image (n = 80, m = 8), corresponding to vessel locations in the registered H&E image. We do not show the corresponding 24 intensity analysis figures.

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