Probing variations of fibrous structures during the development of breast ductal carcinoma tissues via Mueller matrix imaging

Abstract

Recently, we developed a label-free method to probe the microstructural information and optical properties of unstained thin tissue slices based on microscopic Mueller matrix imaging technique. In this paper, we take the microscopic Mueller matrix images of human breast ductal carcinoma tissue samples at different pathological stages, and then calculate and analyze their retardance-related Mueller matrix-derived parameters. To reveal the microstructural features more quantitatively and precisely, we propose a new method based on first-order statistical properties of image to transform the 2D images of Mueller matrix parameters into several statistical feature vectors. We evaluate each statistical feature vector by corresponding classification characteristic value extracted from the statistical features of Mueller matrix parameters images of healthy breast duct tissue samples. The experimental results indicate that these statistical feature vectors of Mueller matrix derived parameters may become powerful tools to quantitatively characterize breast ductal carcinoma tissue samples at different pathological stages. It has the potential to facilitate automating the staging process of breast ductal carcinoma tissue, resulting in the improvement of diagnostic efficiency.

Fig. 1.

Photograph and schematic of the Mueller matrix microscope. P: polarizer; R: quarter-wave plate; PSG: polarization states generator; PSA: polarization states analyzer.

Fig. 2.

Microscopic images of the H and E stained slices of breast ductal tissues at different pathological stages: (a1) stage 1; (b1) stage 1_2; (c1) stage 2; (d1) stage 2_3; (e1) stage 3, and the corresponding 12-µm-thick unstained dewaxed slices of breast ductal tissues: (a2) stage 1; (b2) stage 1_2; (c2) stage 2; (d2) stage 2_3; (e2) stage 3.

Fig. 3.

Process of 2D polarization image digitization: (a) Pseudo-color image of Mueller matrix parameter |m43| of a 12-µm-thick unstained dewaxed slice of breast ductal carcinoma in situ. Different blocks are marked by white squares; (b) Diagram of the method for getting six statistical feature vectors from a polarimetric image.

Fig. 4.

Pseudo-color images of polarimetric parameters (the absolute value of Mueller matrix element |m43|, MMT parameter t and MMPD parameter δ) of the 12-µm-thick unstained dewaxed slices of breast duct tissue samples at different pathological stages: (a) stage 1 (normal); (b) stage 1_2 (between normal and ductal carcinoma in situ); (c) stage 2 (ductal carcinoma in situ); (d) stage 2_3 (between ductal carcinoma in situ and invasive ductal carcinoma); (e) stage 3 (invasive ductal carcinoma). The boundaries of the breast ducts are marked by the white dotted lines. The range of color bar is from 2th percentile to 98th percentile of each polarimetric parameter of all of breast ductal tissues at different pathological stages.

Fig. 5.

Components of six statistical feature vectors extracted from |m43| of breast ductal carcinoma tissue samples at different pathological stages: (a) stage 1_2; (b) stage 2; (c) stage 2_3; (d) stage 3. Classification characteristic values obtained from normal tissue samples are denoted by black or red solid lines for each statistical feature vector. 378 components of statistical feature vectors are denoted as point clouds in different colors next to the corresponding box plots.

Fig. 6.

Values of N_parameters of one sample in each pathological stage (from stage 1 to stage 3) for three polarimetric parameters: (a) absolute value of Mueller matrix element |m43|; (b) MMT parameter t; (c) MMPD parameter δ. The values of N_parameters in stage 1 are set as 1 used as criteria for evaluating other stages.

Fig. 7.

Box plots of N_parameters of 10 samples in each pathological stage (from stage 1 to stage 3) for three polarimetric parameters: (a) absolute value of Mueller matrix element |m43|; (b) MMT parameter t; (c) MMPD parameter δ. The values of N_parameters in stage 1 are set as 1 used as criteria for evaluating other stages.