Classification and analysis of human ovarian tissue using full field optical coherence tomography

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Grading of glioma tumors using digital holographic microscopy

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Depth-Resolved Attenuation Mapping of the Vaginal Wall under Prolapse and after Laser Treatment Using Cross-Polarization Optical Coherence Tomography: A Pilot Study

Vaginal wall prolapse is the most common type of pelvic organ prolapse and is mainly associated with collagen bundle changes in the lamina propria. Neodymium (Nd:YAG) laser treatment was used as an innovative, minimally invasive and non-ablative procedure for the treatment of early-stage vaginal wall prolapse. The purpose of this pilot study was to assess connective tissue changes in the vaginal wall under prolapse without treatment and after Nd:YAG laser treatment using cross-polarization optical coherence tomography (CP OCT) with depth-resolved attenuation mapping. A total of 26 freshly excised samples of vaginal wall from 26 patients with age norm (n = 8), stage I-II prolapses without treatment (n = 8) and stage I-II prolapse 1-2 months after Nd:YAG laser treatment (n = 10) were assessed. As a result, for the first time, depth-resolved attenuation maps of the vaginal wall in the B-scan projection in the co- and cross-polarization channels were constructed. Two parameters within the lamina propria were target calculated: the median value and the percentages of high (≥4 mm-1) and low (<4 mm-1) attenuation coefficient values. A significant (p < 0.0001) decrease in the parameters in the case of vaginal wall prolapse compared to the age norm was identified. After laser treatment, a significant (p < 0.0001) increase in the parameters compared to the normal level was also observed. Notably, in the cross-channel, both parameters showed a greater difference between the groups than in the co-channel. Therefore, using the cross-channel achieved more reliable differentiation between the groups. To conclude, attenuation coefficient maps allow visualization and quantification of changes in the condition of the connective tissue of the vaginal wall. In the future, CP OCT could be used for in vivo detection of early-stage vaginal wall prolapse and for monitoring the effectiveness of treatment.

Enhanced 3D visualization of human fallopian tube morphology using a miniature optical coherence tomography catheter

We demonstrate the use of our miniature optical coherence tomography catheter to acquire three-dimensional human fallopian tube images. Images of the fallopian tube's tissue morphology, vasculature, and tissue heterogeneity distribution are enhanced by adaptive thresholding, masking, and intensity inverting, making it easier to differentiate malignant tissue from normal tissue. The results show that normal fallopian tubes tend to have rich vasculature accompanied by a patterned tissue scattering background, features that do not appear in malignant cases. This finding suggests that miniature OCT catheters may have great potential for fast optical biopsy of the fallopian tube.

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.

Methods and applications of full-field optical coherence tomography: a review

SIGNIFICANCE

Full-field optical coherence tomography (FF-OCT) enables en face views of scattering samples at a given depth with subcellular resolution, similar to biopsy without the need of sample slicing or other complex preparation. This noninvasive, high-resolution, three-dimensional (3D) imaging method has the potential to become a powerful tool in biomedical research, clinical applications, and other microscopic detection.

AIM

Our review provides an overview of the disruptive innovations and key technologies to further improve FF-OCT performance, promoting FF-OCT technology in biomedical and other application scenarios.

APPROACH

A comprehensive review of state-of-the-art accomplishments in OCT has been performed. Methods to improve performance of FF-OCT systems are reviewed, including advanced phase-shift approaches for imaging speed improvement, methods of denoising, artifact reduction, and aberration correction for imaging quality optimization, innovations for imaging flux expansion (field-of-view enlargement and imaging-depth-limit extension), new implementations for multimodality systems, and deep learning enhanced FF-OCT for information mining, etc. Finally, we summarize the application status and prospects of FF-OCT in the fields of biomedicine, materials science, security, and identification.

RESULTS

The most worth-expecting FF-OCT innovations include combining the technique of spatial modulation of optical field and computational optical imaging technology to obtain greater penetration depth, as well as exploiting endogenous contrast for functional imaging, e.g., dynamic FF-OCT, which enables noninvasive visualization of tissue dynamic properties or intracellular motility. Different dynamic imaging algorithms are compared using the same OCT data of the colorectal cancer organoid, which helps to understand the disadvantages and advantages of each. In addition, deep learning enhanced FF-OCT provides more valuable characteristic information, which is of great significance for auxiliary diagnosis and organoid detection.

CONCLUSIONS

FF-OCT has not been completely exploited and has substantial growth potential. By elaborating the key technologies, performance optimization methods, and application status of FF-OCT, we expect to accelerate the development of FF-OCT in both academic and industry fields. This renewed perspective on FF-OCT may also serve as a road map for future development of invasive 3D super-resolution imaging techniques to solve the problems of microscopic visualization detection.

MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma

OBJECTIVES

To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and compare its predictive performance with subjective radiologists' assessment.

METHODS

We retrospectively enrolled 124 patients with pathologically proven RB (90 in training set and 34 in validation set) who had MRI scans before surgery. A radiomics model for predicting PLONI was developed by extracting quantitative imaging features from axial T2W images and contrast-enhanced T1W images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, where upon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance in the training and validation set. The performance of the radiomics model was compared to radiologists' assessment by DeLong test.

RESULTS

The AUC of the radiomics model for the prediction of PLONI was 0.928 in the training set and 0.841 in the validation set. Radiomics model produced better sensitivity than radiologists' assessment (81.1% vs  43.2% in training set, 82.4vs 52.9% in validation set). In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists' assessment was 0.674 (p < 0.001, DeLong test).

CONCLUSION

MRI-based radiomics model to predict PLONI in RB patients was shown to be superior to visual assessment with improved sensitivity and AUC, and may serve as a potential tool to guide personalized treatment.

Ovarian cancer detection using optical coherence tomography and convolutional neural networks

Ovarian cancer has the sixth-largest fatality rate in the United States among all cancers. A non-surgical assay capable of detecting ovarian cancer with acceptable sensitivity and specificity has yet to be developed. However, such a discovery would profoundly impact the pace of the treatment and improvement to patients' quality of life. Achieving such a solution requires high-quality imaging, image processing, and machine learning to support an acceptably robust automated diagnosis. In this work, we propose an automated framework that learns to identify ovarian cancer in transgenic mice from optical coherence tomography (OCT) recordings. Classification is accomplished using a neural network that perceives spatially ordered sequences of tomograms. We present three neural network-based approaches, namely a VGG-supported feed-forward network, a 3D convolutional neural network, and a convolutional LSTM (Long Short-Term Memory) network. Our experimental results show that our models achieve a favorable performance with no manual tuning or feature crafting, despite the challenging noise inherent in OCT images. Specifically, our best performing model, the convolutional LSTM-based neural network, achieves a mean AUC (± standard error) of 0.81 ± 0.037. To the best of the authors' knowledge, no application of machine learning to analyze depth-resolved OCT images of whole ovaries has been documented in the literature. A significant broader impact of this research is the potential transferability of the proposed diagnostic system from transgenic mice to human organs, which would enable medical intervention from early detection of an extremely deadly affliction.

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm

OBJECTIVE

To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).

METHODS

A total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.

RESULTS

Attenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.

CONCLUSION

It is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.

Liver tissue classification of en face images by fractal dimension-based support vector machine

Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is proposed with en face tomographic images. Twenty samples (10 normal and 10 cancerous) were operated from humans and composed of 285 en face tomographic images. Six histogram features and one proposed fractal dimension parameter that reveal the refractive index inhomogeneities of tissue were extracted and made up the training set. The other different 16 samples (8 normal and 8 cancerous) were imaged (190 images) and employed as the test set with the same features. First, a subcellular-resolution tomographic image library for four histopathological areas in liver tissue was established. Second, the area under the receiver operating characteristics of 0.9378, 0.9858, 0.9391, 0.9517 for prediction of the cancerous hepatic cell, central vein, fibrosis, and portal vein were measured with the test set. The results indicate that the proposed classifier from FF-OCT images shows promise as a label-free assessment of quantified tumor detection, suggesting the fractal dimension-based classifier could aid clinicians in detecting tumor boundaries for resection in surgery in the future.

High-resolution microscopy for imaging cancer pathobiology

PURPOSE OF REVIEW

Light microscopy plays an essential role in clinical diagnosis and understanding the pathogenesis of cancer. Conventional bright-field microscope is used to visualize abnormality in tissue architecture and nuclear morphology, but often suffers from many limitations. This review focuses on the potential of new imaging techniques to improve basic and clinical research in pathobiology.

RECENT FINDINGS

Light microscopy has significantly expanded its ability in resolution, imaging volume, speed and contrast. It now allows 3D high-resolution volumetric imaging of tissue architecture from large tissue and molecular structures at nanometer resolution.

SUMMARY

Pathologists and researchers now have access to various imaging tools to study cancer pathobiology in both breadth and depth. Although clinical adoption of a new technique is slow, the new imaging tools will provide significant new insights and open new avenues for improving early cancer detection, personalized risk assessment and identifying the best treatment strategies.

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography

In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32 % ± 1.2 % . Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 94.8 % ± 1.2 % compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

The clinical usefulness of optical coherence tomography during cancer interventions

INTRODUCTION

Tumor detection and visualization plays a key role in the clinical workflow of a patient with suspected cancer, both in the diagnosis and treatment. Several optical imaging techniques have been evaluated for guidance during oncological interventions. Optical coherence tomography (OCT) is a technique which has been widely evaluated during the past decades. This review aims to determine the clinical usefulness of OCT during cancer interventions focussing on qualitative features, quantitative features and the diagnostic value of OCT.

METHODS

A systematic literature search was performed for articles published before May 2018 using OCT in the field of surgical oncology. Based on these articles, an overview of the clinical usefulness of OCT was provided per tumor type.

RESULTS

A total of 785 articles were revealed by our search, of which a total of 136 original articles were available for analysis, which formed the basis of this review. OCT is currently utilised for both preoperative diagnosis and intraoperative detection of skin, oral, lung, breast, hepatobiliary, gastrointestinal, urological, and gynaecological malignancies. It showed promising results in tumor detection on a microscopic level, especially using higher resolution imaging techniques, such as high-definition OCT and full-field OCT.

CONCLUSION

In the near future, OCT could be used as an additional tool during bronchoscopic or endoscopic interventions and could also be implemented in margin assessment during (laparoscopic) cancer surgery if a laparoscopic or handheld OCT device will be further developed to make routine clinical use possible.

Polarization-sensitive optical coherence tomography reveals gray matter and white matter atrophy in SCA1 mouse models

Spinocerebellar ataxia type 1 (SCA1) is a fatal inherited neurodegenerative disease. In this study, we demonstrate the label-free optical imaging methodology that can detect, with a high degree of sensitivity, discrete areas of degeneration in the cerebellum of the SCA1 mouse models. We used ATXN1[82Q] and ATXN1[30Q]-D776 mice in which the transgene is directed only to Purkinje cells. Molecular layer, granular layer, and white matter regions are analyzed using the intrinsic contrasts provided by polarization-sensitive optical coherence tomography. Cerebellar atrophy in SCA1 mice occurred both in gray matter and white matter. While gray matter atrophy is obvious, indications of white matter atrophy including different birefringence characteristics, and shortened and contorted branches are observed. Imaging results clearly show the loss or atrophy of myelinated axons in ATXN1[82Q] mice. The method provides unbiased contrasts that can facilitate the understanding of the pathological progression in neurodegenerative diseases and other neural disorders.

Quantitative multispectral ex vivo optical evaluation of human ovarian tissue using spatial frequency domain imaging

About 85-90% of all ovarian cancers are carcinomas; these manifest clinically as mass-forming epithelial proliferations involving the ovary. In this study, a visible light spatial frequency domain imaging (SFDI) system was used for multispectral ex vivo imaging and quantitative evaluation of freshly excised benign and malignant human ovarian tissues. A total of 14 ovaries from 11 patients undergoing oophorectomy were investigated. Using a logistic regression model with seven significant spectral and spatial features extracted from SFDI images, a sensitivity of 94.06% and specificity of 93.53% were achieved for prediction of histologically confirmed invasive carcinoma.

Preliminary human application of optical coherence tomography for quantification and localization of primordial follicles aimed at effective ovarian tissue transplantation

Purpose

The purpose of this study was to evaluate the possible clinical application of optical coherence tomography for assessing ovarian reserve in individual specimens of human ovarian tissue for fertility preservation.

Methods

Ovarian tissue examination by optical coherence tomography was performed before ovarian tissue cryopreservation. Three of the four subjects had hematological disease or cancer, and they faced a threat to their fertility due to impending chemotherapy. One patient underwent ovarian tissue extraction for in vitro activation of dormant follicles as fertility treatment.

Results

The current full-field optical coherence tomography technique can detect primordial follicles in non-fixed and non-embedded human ovarian tissue. These images are well correlated with histological evaluation and the ovarian reserve test, including follicle counts.

Conclusion

It was demonstrated that optical coherence tomography could assess localization of primordial follicles and ovarian reserve in specimens of non-fixed human ovarian cortex, although optimization for examination of human ovarian tissue is needed for clinical application. Additionally, this technique holds the possibility of assessing the ovarian reserve of patients with unevaluable ovarian reserve.

Trial registration number

UMIN000023141

Optical coherence tomography in gynecology: a narrative review

Modern gynecologic practice requires noninvasive diagnostics techniques capable of detecting morphological and functional alterations in tissues of female reproductive organs. Optical coherence tomography (OCT) is a promising tool for providing imaging of biotissues with high resolution at depths up to 2 mm. Design of the customized probes provides wide opportunities for OCT use in gynecology. This paper contains a retrospective insight into the history of OCT employment in gynecology, an overview of the existing gynecologic OCT probes, including those for combination with other diagnostic modalities, and state-of-the-art application of OCT for diagnostics of tumor and nontumor pathologies of female genitalia. Perspectives of OCT both in diagnostics and treatment planning and monitoring in gynecology are overviewed.

Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier's input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.