Collagen provides tissue strength and structural integrity. Quantification of the orientated dispersion of collagen fibers is an important factor when studying the mechanical properties of the cervix. In this study, for the first time, a new method for rapid characterization of the collagen fiber orientations of the cervix using linearly polarized light colposcopy is presented. A total of 24 colposcopic images were captured using a cross-polarized imaging system with white LED light sources. In the preprocessing stage, the Red channel of the RGB image was chosen, which contains no information of the blood vessels because of the low-absorption of blood cells in the red region. OrientationJ, which is an ImageJ plug-in, was used to estimate the local orientation of the collagen fibers. The result shows that in the nonpregnant cervix, the middle zone (Zone 2) has circumferentially aligned collagen fibers while the inner zone (Zone 1) has randomly arranged. The collagen fiber dispersion in Zone 2 is much smaller than that in Zone 1 at all four quadrants region (anterior, posterior, left, and right quadrant). This new analysis technique could potentially combine with diagnostic tools to provide a quantitative platform of collagen fibers in the clinic.

Ovarian cancer is one of the most aggressive and heterogeneous female tumors in the world, and serous ovarian cancer (SOC) is of particular concern for being the leading cause of ovarian cancer death. Due to its clinical and biological complexities, ovarian cancer is still considered one of the most difficult tumors to diagnose and manage. In this study, three datasets were assembled, including 30 cases of serous cystadenoma (SCA), 30 cases of serous borderline tumor (SBT), and 45 cases of serous adenocarcinoma (SAC). Mueller matrix microscopy is used to obtain the polarimetry basis parameters (PBPs) of each case, combined with a machine learning (ML) model to derive the polarimetry feature parameters (PFPs) for distinguishing serous ovarian tumor (SOT). The correlation between the mean values of PBPs and the clinicopathological features of serous ovarian cancer was analyzed. The accuracies of PFPs obtained from three types of SOT for identifying dichotomous groups (SCA versus SAC, SCA versus SBT, and SBT versus SAC) were 0.91, 0.92, and 0.8, respectively. The accuracy of PFP for identifying triadic groups (SCA versus SBT versus SAC) was 0.75. Correlation analysis between PBPs and the clinicopathological features of SOC was performed. There were correlations between some PBPs (δ, β, qL, E2, rqcross, P2, P3, P4, and P5) and clinicopathological features, including the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grading, preoperative ascites, malignant ascites, and peritoneal implantation. The research showed that PFPs extracted from polarization images have potential applications in quantitatively differentiating the SOTs. These polarimetry basis parameters related to the clinicopathological features of SOC can be used as prognostic factors.

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration surgery. Quantitative evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis techniques. However, the distinguishment of conjunctiva, sclera and the scar tissue in the surgical area still relies on pathologists’ experience. Since polarized light imaging is sensitive to anisotropic properties of the media, it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion, organization and the orientation of the fibers. In this paper, we defined the conjunctiva, sclera, and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix microscope. Discrimination score based on parameters derived from Mueller matrix and machine learning was calculated and tested as a diagnostic index. As a result, the discrimination score of three target tissues showed significant difference between each other (p<0.001). The visualization of the discrimination results showed significant contrast between target tissues. This study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.

The miniaturized femtosecond laser in near infrared-II region is the core equipment of three-photon microscopy. In this paper, we design a compact and robust illumination source that emits dual-color linearly polarized light for three-photon microscopy. Based on an all-polarization-maintaining passive mode-locked fiber laser, we shift the center wavelength of the pulses to the 1.7μm band utilizing cascade Raman effect, thereby generate dual-wavelength pulses. To enhance clarity, the two wavelengths are separated through the graded-index multimode fiber. Then we obtain the dual-pulse sequences with 1639.4nm and 1683.7nm wavelengths, 920fs pulse duration, and 23.75MHz pulse repetition rate. The average power of the signal is 53.64mW, corresponding to a single pulse energy of 2.25nJ. This illumination source can be further amplified and compressed for three-photon fluorescence imaging, especially dual-color three-photon fluorescence imaging, making it an ideal option for biomedical applications.

Photoacoustic microscopy (PAM), due to its deep penetration depth and high contrast, is playing an increasingly important role in biomedical imaging. PAM imaging systems equipped with conventional ultrasound transducers have demonstrated excellent imaging performance. However, these opaque ultrasonic transducers bring some constraints to the further development and application of PAM, such as complex optical path, bulky size, and difficult to integrate with other modalities. To overcome these problems, ultrasonic transducers with high optical transparency have appeared. At present, transparent ultrasonic transducers are divided into optical-based and acoustic-based sensors. In this paper, we mainly describe the acoustic-based piezoelectric transparent transducers in detail, of which the research advances in PAM applications are reviewed. In addition, the potential challenges and developments of transparent transducers in PAM are also demonstrated.

Polarimetry encompasses a collection of optical techniques broadly used in a variety of fields. Nowadays, such techniques have provided their suitability in the biomedical field through the study of the polarimetric response of biological samples (retardance, dichroism and depolarization) by measuring certain polarimetric observables. One of these features, depolarization, is mainly produced by scattering on samples, which is a predominant effect in turbid media as biological tissues. In turn, retardance and dichroic effects are produced by tissue anisotropies and can lead to depolarization too. Since depolarization is a predominant effect in tissue samples, we focus on studying different depolarization metrics for biomedical applications. We report the suitability of a set of depolarizing observables, the indices of polarimetric purity (IPPs), for biological tissue inspection. We review some results where we demonstrate that IPPs lead to better performance than the depolarization index, which is a well-established and commonly used depolarization observable in the literature. We also provide how IPPs are able to significantly enhance contrast between different tissue structures and even to reveal structures hidden by using standard intensity images. Finally, we also explore the classificatory potential of IPPs and other depolarizing observables for the discrimination of different tissues obtained from ex vivo chicken samples (muscle, tendon, myotendinous junction and bone), reaching accurate models for tissue classification.

The nonuniform distribution of interference spectrum in wavenumber k-space is a key issue to limit the imaging quality of Fourier-domain optical coherence tomography (FD-OCT). At present, the reconstruction quality at different depths among a variety of processing methods in k-space is still uncertain. Using simulated and experimental interference spectra at different depths, the effects of common six processing methods including uniform resampling (linear interpolation (LI), cubic spline interpolation (CSI), time-domain interpolation (TDI), and K-B window convolution) and nonuniform sampling direct-reconstruction (Lomb periodogram (LP) and nonuniform discrete Fourier transform (NDFT)) on the reconstruction quality of FD-OCT were quantitatively analyzed and compared in this work. The results obtained by using simulated and experimental data were coincident. From the experimental results, the averaged peak intensity, axial resolution, and signal-to-noise ratio (SNR) of NDFT at depth from 0.5 to 3.0mm were improved by about 1.9dB, 1.4 times, and 11.8dB, respectively, compared to the averaged indices of all the uniform resampling methods at all depths. Similarly, the improvements of the above three indices of LP were 2.0dB, 1.4 times, and 11.7dB, respectively. The analysis method and the results obtained in this work are helpful to select an appropriate processing method in k-space, so as to improve the imaging quality of FD-OCT.

The zebrafish embryos were widely employed in genetics, development and drug discovery studies as miniatured animal models. Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency. Here, we reported a high-throughput sorting system for two-color fluorescent zebrafish embryos. The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity. The two-color fluorescent signals were split into two lines and detected by an area array camera. The system achieves the sorting of 100 embryos in less than 10min with an accuracy of greater than 95%.

Automatic cell counting provides an effective tool for medical research and diagnosis. Currently, cell counting can be completed by transmitted-light microscope, however, it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells. Further, the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope, automatically and effectively. In this work, a new deep-learning (DL)-based two-stage detection method (cGAN-YOLO) is designed to further enhance the performance of cell counting, which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model. The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images. Compared with the previously reported YOLO-based one-stage detection method, high recognition accuracy (RA) is achieved by the cGAN-YOLO method, with an improvement of 29.80%. Furthermore, we can also observe that cGAN-YOLO obtains an improvement of 12.11% in RA compared with the previously reported image-translation-based detection method. In a word, cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance, which extends the applicability in clinical research.

Laser speckle contrast imaging (LSCI) is a powerful tool for monitoring blood flow changes in tissue or vessels in vivo, but its applications are limited by shallow penetration depth under reflective imaging configuration. The traditional LSCI setup has been used in transmissive imaging for depth extension up to 2lt–3lt (lt is the transport mean free path), but the blood flow estimation is biased due to the depth uncertainty in large depth of field (DOF) images. In this study, we propose a transmissive multifocal LSCI for depth-resolved blood flow in thick tissue, further extending the transmissive LSCI for tissue thickness up to 12lt. The limited-DOF imaging system is applied to the multifocal acquisition, and the depth of the vessel is estimated using a robust visibility parameter Vr in the coherent domain. The accuracy and linearity of depth estimation are tested by Monte Carlo simulations. Based on the proposed method, the model of contrast analysis resolving the depth information is established and verified in a phantom experiment. We demonstrated its effectiveness in acquiring depth-resolved vessel structures and flow dynamics in in vivo imaging of chick embryos.

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors arising in the digest tract. It brings a challenge to diagnosis because it is asymptomatic clinically. It is well known that tumor development is often accompanied by the changes in the morphology of collagen fibers. Nowadays, an emerging optical imaging technique, second-harmonic generation (SHG), can directly identify collagen fibers without staining due to its noncentrosymmetric properties. Therefore, in this study, we attempt to assess the feasibility of SHG imaging for detecting GISTs by monitoring the morphological changes of collagen fibers in tumor microenvironment. We found that collagen alterations occurred obviously in the GISTs by comparing with normal tissues, and furthermore, two morphological features from SHG images were extracted to quantitatively assess the morphological difference of collagen fibers between normal muscular layer and GISTs by means of automated image analysis. Quantitative analyses show a significant difference in the two collagen features. This study demonstrates the potential of SHG imaging as an adjunctive diagnostic tool for label-free identification of GISTs.