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Extracting morphologies from third harmonic generation images of structurally normal human brain tissue
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Bioinformatics, 33, 2017, 11, S. 1712-1720 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| author_facet |
Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C |
|---|---|
| author |
Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C |
| spellingShingle |
Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C Bioinformatics Extracting morphologies from third harmonic generation images of structurally normal human brain tissue Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
zhang, zhiqing |
| spelling |
Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btx035 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components—brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and Implementation</jats:title> <jats:p>The software and test datasets are available from the authors.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> Extracting morphologies from third harmonic generation images of structurally normal human brain tissue Bioinformatics |
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| title |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_unstemmed |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_full |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_fullStr |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_full_unstemmed |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_short |
Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_sort |
extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btx035 |
| publishDate |
2017 |
| physical |
1712-1720 |
| description |
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Motivation</jats:title>
<jats:p>The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components—brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and Implementation</jats:title>
<jats:p>The software and test datasets are available from the authors.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
</jats:sec> |
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| author | Zhang, Zhiqing, Kuzmin, Nikolay V, Groot, Marie Louise, de Munck, Jan C |
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| description | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components—brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and Implementation</jats:title> <jats:p>The software and test datasets are available from the authors.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> |
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| spelling | Zhang, Zhiqing Kuzmin, Nikolay V Groot, Marie Louise de Munck, Jan C 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btx035 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components—brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and Implementation</jats:title> <jats:p>The software and test datasets are available from the authors.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> Extracting morphologies from third harmonic generation images of structurally normal human brain tissue Bioinformatics |
| spellingShingle | Zhang, Zhiqing, Kuzmin, Nikolay V, Groot, Marie Louise, de Munck, Jan C, Bioinformatics, Extracting morphologies from third harmonic generation images of structurally normal human brain tissue, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_full | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_fullStr | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_full_unstemmed | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_short | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_sort | extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| title_unstemmed | Extracting morphologies from third harmonic generation images of structurally normal human brain tissue |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btx035 |