Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

Abstract EN

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.