Yang Dong

SignificanceMueller matrix (MM) microscopy has proven to be a powerful tool for probing microstructural characteristics of biological samples down to subwavelength scale. However, in clinical practice, doctors usually rely on bright-field microscopy images of stained tissue slides to identify characteristic features of specific diseases and make accurate diagnosis. Cross-modality translation based on polarization imaging helps to improve the efficiency and stability in analyzing sample properties from different modalities for pathologists.AimIn this work, we propose a computational image translation technique based on deep learning to enable bright-field microscopy contrast using snapshot Stokes images of stained pathological tissue slides. Taking Stokes images as input instead of MM images allows the translated bright-field images to be unaffected by variations of light source and samples.ApproachWe adopted CycleGAN as the translation model to avoid requirements on co-registered image pairs in the training. This method can generate images that are equivalent to the bright-field images with different staining styles on the same region.ResultsPathological slices of liver and breast tissues with hematoxylin and eosin staining and lung tissues with two types of immunohistochemistry staining, i.e., thyroid transcription factor-1 and Ki-67, were used to demonstrate the effectiveness of our method. The output results were evaluated by four image quality assessment methods.ConclusionsBy comparing the cross-modality translation performance with MM images, we found that the Stokes images, with the advantages of faster acquisition and independence from light intensity and image registration, can be well translated to bright-field images.

Polarization imaging is sensitive to sub-wavelength microstructures of various cancer tissues, providing abundant optical characteristics and microstructure information of complex pathological specimens. However, how to reasonably utilize polarization information to strengthen pathological diagnosis ability remains a challenging issue. In order to take full advantage of pathological image information and polarization features of samples, we propose a dual polarization modality fusion network (DPMFNet), which consists of a multi-stream CNN structure and a switched attention fusion module for complementarily aggregating the features from different modality images. Our proposed switched attention mechanism could obtain the joint feature embeddings by switching the attention map of different modality images to improve their semantic relatedness. By including a dual-polarization contrastive training scheme, our method can synthesize and align the interaction and representation of two polarization features. Experimental evaluations on three cancer datasets show the superiority of our method in assisting pathological diagnosis, especially in small datasets and low imaging resolution cases. Grad-CAM visualizes the important regions of the pathological images and the polarization images, indicating that the two modalities play different roles and allow us to give insightful corresponding explanations and analysis on cancer diagnosis conducted by the DPMFNet. This technique has potential to facilitate the performance of pathological aided diagnosis and broaden the current digital pathology boundary based on pathological image features.

High-grade squamous intraepithelial lesion (HSIL) is regarded as a serious precancerous state of cervix, and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis and treatment of cervical lesions. Pathologists examine the biopsied cervical epithelial tissue through a microscope. The pathological examination will take a long time and sometimes results in high inter- and intra-observer variability in outcomes. Polarization imaging techniques have broad application prospects for biomedical diagnosis such as breast, liver, colon, thyroid and so on. In our team, we have derived polarimetry feature parameters (PFPs) to characterize microstructural features in histological sections of breast tissues, and the accuracy for PFPs ranges from 0.82 to 0.91. Therefore, the aim of this paper is to distinguish automatically microstructural features between HSIL and cervical squamous cell carcinoma (CSCC) by means of polarization imaging techniques, and try to provide quantitative reference index for pathological diagnosis which can alleviate the workload of pathologists. Polarization images of the H&E stained histological slices were obtained by Mueller matrix microscope. The typical pathological structure area was labeled by two experienced pathologists. Calculate the polarimetry basis parameter (PBP) statistics for this region. The PBP statistics (stat_PBPs) are screened by mutual information (MI) method. The training method is based on a linear discriminant analysis (LDA) classifier which finds the most simplified linear combination from these stat_PBPs and the accuracy remains constant to characterize the specific microstructural feature quantitatively in cervical squamous epithelium. We present results from 37 clinical patients with analysis regions of cervical squamous epithelium. The accuracy of PFP for recognizing HSIL and CSCC was 83.8% and 87.5%, respectively. This work demonstrates the ability of PFP to quantitatively characterize the cervical squamous epithelial lesions in the H&E pathological sections. Significance: Polarization detection technology provides an efficient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections.

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis.

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.

This paper is based on the Photoplethysmography technology to measure the arterial blood oxygen saturation (SpO2) and Heart rate which are people's vital sign. In the system we design a non-invasive continuous and portable wearable watch to measure human SpO2 and Heart rate. The wearable watch uses the micro-optoelectronic sensors to get PPG signal and utilizes wireless Bluetooth 4.0 to communicate with PC or mobile phone. The principle of Photoplethysmography is that reflection or transmission light changes with blood volume in the microvascular of tissue. So the PPG signal contains much valuable information about cardiovascular system such as SpO2, heart rate, blood pressure, and respiratory rate. The most advantages of PPG is non-invasive and low-cost, however the transmitting and receiving channel of PPG is easily disturbed by outside light and motion. In this Paper, to minimize the PPG channel noise and motion artifacts, we build the PPG signal channel model and use sensor to measure the outside light to cancel the distance disturbance between micro-optoelectronic sensors and skin. Because of the motion noises falls within the same frequency band as the physiological signal, we use MEMS approach to measure the motion information and cancel the disturbance. Disease about cardiovascular will be monitored and predicted in future work.