Limits...
Epithelial Ovarian Cancer Diagnosis of Second-Harmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol

View Article: PubMed Central - PubMed

ABSTRACT

A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fast Fourier transform analysis and the gray-level co-occurrence matrix). These were applied to analyze biopsy samples of human ovarian epithelial cancer at different stages of malignancy (mucinous, serous, mixed, and endometrial subtypes). The semiautomation procedure enabled us to design a diagnostic protocol that recognizes between healthy and pathologic tissues, as well as between different tumor types.

No MeSH data available.


Related in: MedlinePlus

Decision tree for ovarian cancer detection using gray-level co-occurrence matrix (GLCM) features extracted from steps 3, 5, 10, and 15 for correlation and from steps 10, 20, 30, and 40 for contrast, energy, and entropy. AR indicates aspect ratio; Contr., contrast; Correl., correlation; E, endometrioid; M, mixed; MA, mucinous adenoma; MAC, mucinous adenocarcinoma; N, normal; SA, serous adenoma; SAC, serous adenocarcinoma; SB, serous borderline; Sn, step n; vs, versus. [ ] indicates confidence interval; ⊂, included in.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5392028&req=5

f5-10.1177_1176935117690162: Decision tree for ovarian cancer detection using gray-level co-occurrence matrix (GLCM) features extracted from steps 3, 5, 10, and 15 for correlation and from steps 10, 20, 30, and 40 for contrast, energy, and entropy. AR indicates aspect ratio; Contr., contrast; Correl., correlation; E, endometrioid; M, mixed; MA, mucinous adenoma; MAC, mucinous adenocarcinoma; N, normal; SA, serous adenoma; SAC, serous adenocarcinoma; SB, serous borderline; Sn, step n; vs, versus. [ ] indicates confidence interval; ⊂, included in.

Mentions: Decision tree for classification detection using AR and GLCM features is shown in Figure 5.


Epithelial Ovarian Cancer Diagnosis of Second-Harmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol
Decision tree for ovarian cancer detection using gray-level co-occurrence matrix (GLCM) features extracted from steps 3, 5, 10, and 15 for correlation and from steps 10, 20, 30, and 40 for contrast, energy, and entropy. AR indicates aspect ratio; Contr., contrast; Correl., correlation; E, endometrioid; M, mixed; MA, mucinous adenoma; MAC, mucinous adenocarcinoma; N, normal; SA, serous adenoma; SAC, serous adenocarcinoma; SB, serous borderline; Sn, step n; vs, versus. [ ] indicates confidence interval; ⊂, included in.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5392028&req=5

f5-10.1177_1176935117690162: Decision tree for ovarian cancer detection using gray-level co-occurrence matrix (GLCM) features extracted from steps 3, 5, 10, and 15 for correlation and from steps 10, 20, 30, and 40 for contrast, energy, and entropy. AR indicates aspect ratio; Contr., contrast; Correl., correlation; E, endometrioid; M, mixed; MA, mucinous adenoma; MAC, mucinous adenocarcinoma; N, normal; SA, serous adenoma; SAC, serous adenocarcinoma; SB, serous borderline; Sn, step n; vs, versus. [ ] indicates confidence interval; ⊂, included in.
Mentions: Decision tree for classification detection using AR and GLCM features is shown in Figure 5.

View Article: PubMed Central - PubMed

ABSTRACT

A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fast Fourier transform analysis and the gray-level co-occurrence matrix). These were applied to analyze biopsy samples of human ovarian epithelial cancer at different stages of malignancy (mucinous, serous, mixed, and endometrial subtypes). The semiautomation procedure enabled us to design a diagnostic protocol that recognizes between healthy and pathologic tissues, as well as between different tumor types.

No MeSH data available.


Related in: MedlinePlus