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Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K - Comput Math Methods Med (2015)

Bottom Line: The proposed method was compared with the k-means and fuzzy c-means clustering methods.Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists.Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

View Article: PubMed Central - PubMed

Affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

ABSTRACT
Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

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Related in: MedlinePlus

(a) Whole-slide tissue image sample for Ki-67-stained histology image for a brain tumor. The red box represents a sample for the focus point from the whole-slide tissue. (b) Image captured after 40x magnification for the focus point region.
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fig1: (a) Whole-slide tissue image sample for Ki-67-stained histology image for a brain tumor. The red box represents a sample for the focus point from the whole-slide tissue. (b) Image captured after 40x magnification for the focus point region.

Mentions: Regarding performing PRE, pathologists usually stain the tissue using Ki-67 antigen [4]. After staining the tissue, the pathologists then examine this biopsy tissue to make a diagnosis. This process starts with visualization of the stained tissue using a whole slide under the microscope at low magnifications (1–1.5x). From the whole slide, the focus point regions that are highly concentrated in cancerous cells (stained cells) are identified and localized as shown in Figure 1. Next, for each selected focus point region, the pathologist creates a zoom region until 40x magnification to perform further analysis for these parts. PRE is then carried out for each part.


Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K - Comput Math Methods Med (2015)

(a) Whole-slide tissue image sample for Ki-67-stained histology image for a brain tumor. The red box represents a sample for the focus point from the whole-slide tissue. (b) Image captured after 40x magnification for the focus point region.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352493&req=5

fig1: (a) Whole-slide tissue image sample for Ki-67-stained histology image for a brain tumor. The red box represents a sample for the focus point from the whole-slide tissue. (b) Image captured after 40x magnification for the focus point region.
Mentions: Regarding performing PRE, pathologists usually stain the tissue using Ki-67 antigen [4]. After staining the tissue, the pathologists then examine this biopsy tissue to make a diagnosis. This process starts with visualization of the stained tissue using a whole slide under the microscope at low magnifications (1–1.5x). From the whole slide, the focus point regions that are highly concentrated in cancerous cells (stained cells) are identified and localized as shown in Figure 1. Next, for each selected focus point region, the pathologist creates a zoom region until 40x magnification to perform further analysis for these parts. PRE is then carried out for each part.

Bottom Line: The proposed method was compared with the k-means and fuzzy c-means clustering methods.Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists.Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

View Article: PubMed Central - PubMed

Affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

ABSTRACT
Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

Show MeSH
Related in: MedlinePlus