Deriving Polarimetry Feature Parameters to Characterize Microstructural Features in Histological Sections of Breast Tissues

Objective: Mueller matrix polarimetry technique has been regarded as a powerful tool for probing the microstructural information of tissues. The multiplying of cells and remodeling of collagen fibers in breast carcinoma tissues have been reported to be related to patient survival and prognosis, and they give rise to observable patterns in hematoxylin and eosin (H&E) sections of typical breast tissues (TBTs) that the pathologist can label as three distinctive pathological features (DPFs)-cell nuclei, aligned collagen, and disorganized collagen. The aim of this paper is to propose a pixel-based extraction approach of polarimetry feature parameters (PFPs) using a linear discriminant analysis (LDA) classifier. These parameters provide quantitative characterization of the three DPFs in four types of TBTs. Methods: The LDA-based training method learns to find the most simplified linear combination from polarimetry basis parameters (PBPs) constrained under the accuracy remains constant to characterize the specific microstructural feature quantitatively in TBTs. Results: We present results from a cohort of 32 clinical patients with analysis of 224 regions-of-interest. The characterization accuracy for PFPs ranges from 0.82 to 0.91. Conclusion: This work demonstrates the ability of PFPs to quantitatively characterize the DPFs in the H&E pathological sections of TBTs. Significance: This technique paves the way for automatic and quantitative evaluation of specific microstructural features in histopathological digitalization and computer-aided diagnosis.

Automated summary

This study proposes a pixel-based extraction approach for polarimetry feature parameters using an LDA classifier to quantitatively characterize the three DPFs in TBTs. Experimental results demonstrate the characterization accuracy for PFPs ranging from 0.82 to 0.91.