Computational immunohistochemistry staining on lung tissues based on Mueller matrix microscopy

Lu Si; Naiqi Li; Shan Du; Yang Dong; Shaoxiong Liu; Hui Ma

doi:10.1117/12.2578179

5 March 2021 Computational immunohistochemistry staining on lung tissues based on Mueller matrix microscopy

Lu Si, Naiqi Li, Shan Du, Yang Dong, Shaoxiong Liu, Hui Ma

Proceedings Volume 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics; 116460T (2021) https://doi.org/10.1117/12.2578179
Event: SPIE BiOS, 2021, Online Only

Abstract

We propose a cross-modality method that translates polarimetric images into bright-field. In the lung tissue histological analysis, immunohistochemical (IHC) staining of tissues is widely used to specify particular cellular events especially in precision medicine. In this work, we measured hematoxylin and eosin (HE) stained slices by Mueller matrix (MM) microscopy and then fed polarimetric data into a well-designed generative adversarial network (GAN). The network can generate images that are equivalent to the IHC stained from bright-field microscopy. This will assist pathologists with the real IHC staining procedure and pathological diagnosis. Instead of preparing specimens from scratch, we collected already existing specimens, i.e., the adjacent HE and IHC stained slices from the same tissue volume. We adopted the CycleGAN to learn the translation between unaligned images from two domains. We used a U-Net based generator and a PixelGAN based discriminator in the model. The efficacy of this method was demonstrated on smooth muscle actin (SMA) staining in lung tissue. The results are evaluated by three image quality assessment methods by comparing the generated and real staining images.

Conference Presentation

Citation Download Citation

Lu Si, Naiqi Li, Shan Du, Yang Dong, Shaoxiong Liu, and Hui Ma "Computational immunohistochemistry staining on lung tissues based on Mueller matrix microscopy", Proc. SPIE 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics, 116460T (5 March 2021); https://doi.org/10.1117/12.2578179

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