Extracting morphologies from third harmonic generation images of structurally normal human brain tissue

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Motivation: 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.

Results: 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.
Original languageEnglish
Pages (from-to)1712-1720
JournalBioinformatics
Volume33
Issue number11
DOIs
Publication statusPublished - 1 Jun 2017

Cite this

@article{d82aa0f59e004250bec5a223cf639796,
title = "Extracting morphologies from third harmonic generation images of structurally normal human brain tissue",
abstract = "Motivation: 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.Results: 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.",
author = "Zhiqing Zhang and Kuzmin, {Nikolay V.} and Groot, {Marie Louise} and {de Munck}, {Jan C.}",
year = "2017",
month = "6",
day = "1",
doi = "10.1093/bioinformatics/btx035",
language = "English",
volume = "33",
pages = "1712--1720",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "11",

}

Extracting morphologies from third harmonic generation images of structurally normal human brain tissue. / Zhang, Zhiqing; Kuzmin, Nikolay V.; Groot, Marie Louise; de Munck, Jan C.

In: Bioinformatics, Vol. 33, No. 11, 01.06.2017, p. 1712-1720.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

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AU - Zhang, Zhiqing

AU - Kuzmin, Nikolay V.

AU - Groot, Marie Louise

AU - de Munck, Jan C.

PY - 2017/6/1

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AB - Motivation: 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.Results: 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.

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