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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

Hypoxia gradient analysis.Intensity level analysis produced by the Intensity-Sample-Ray-Bundles algorithm for centers 1 (left 3 panels), 2 (middle 3 panels), and 3 (right 3 panels). Intensity-Sample-Ray-Bundles creates three plots of the data, where the horizontal axis denotes distance from the center (pixels), and the vertical axis denotes intensity level. The first panel shows every ray measurement (light gray), upon which  (blue) and  (red) are overlaid; its title gives rm (pixels). The second panel shows  (blue) ±  (gray), overlaid with segmented least squares fits to  (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The third panel shows  (red) ±  (gray), overlaid with segmented least squares fits to  (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The segmented least square fits are given by a dynamic programming algorithm using a cost parameter C = 200.
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pone.0153623.g003: Hypoxia gradient analysis.Intensity level analysis produced by the Intensity-Sample-Ray-Bundles algorithm for centers 1 (left 3 panels), 2 (middle 3 panels), and 3 (right 3 panels). Intensity-Sample-Ray-Bundles creates three plots of the data, where the horizontal axis denotes distance from the center (pixels), and the vertical axis denotes intensity level. The first panel shows every ray measurement (light gray), upon which (blue) and (red) are overlaid; its title gives rm (pixels). The second panel shows (blue) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The third panel shows (red) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The segmented least square fits are given by a dynamic programming algorithm using a cost parameter C = 200.

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)

Hypoxia gradient analysis.Intensity level analysis produced by the Intensity-Sample-Ray-Bundles algorithm for centers 1 (left 3 panels), 2 (middle 3 panels), and 3 (right 3 panels). Intensity-Sample-Ray-Bundles creates three plots of the data, where the horizontal axis denotes distance from the center (pixels), and the vertical axis denotes intensity level. The first panel shows every ray measurement (light gray), upon which  (blue) and  (red) are overlaid; its title gives rm (pixels). The second panel shows  (blue) ±  (gray), overlaid with segmented least squares fits to  (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The third panel shows  (red) ±  (gray), overlaid with segmented least squares fits to  (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The segmented least square fits are given by a dynamic programming algorithm using a cost parameter C = 200.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153623.g003: Hypoxia gradient analysis.Intensity level analysis produced by the Intensity-Sample-Ray-Bundles algorithm for centers 1 (left 3 panels), 2 (middle 3 panels), and 3 (right 3 panels). Intensity-Sample-Ray-Bundles creates three plots of the data, where the horizontal axis denotes distance from the center (pixels), and the vertical axis denotes intensity level. The first panel shows every ray measurement (light gray), upon which (blue) and (red) are overlaid; its title gives rm (pixels). The second panel shows (blue) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The third panel shows (red) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (l, pixels), slope (s), and least squares error (e, pixels) for each fitted segment. The segmented least square fits are given by a dynamic programming algorithm using a cost parameter C = 200.
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