Singular Nuclei Segmentation for Automatic Quantification Using CISH Whole Slide Images.

Human epidermal growth factor receptor 2 () quantification is performed routinely for all breast cancer patients to determine their suitability for -targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for quantification in which at least 20 cancer-affected singular nuclei are quantified for grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic quantification during a comparison with the SVM-based approach.