Assessment of hessian-based Frangi vesselness filter in optoacoustic imaging

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

In vivo noninvasive systemic myography of acute systemic vasoactivity in female pregnant mice

Altered vasoactivity is a major characteristic of cardiovascular and oncological diseases, and many therapies are therefore targeted to the vasculature. Therapeutics which are selective for the diseased vasculature are ideal, but whole-body selectivity of a therapeutic is challenging to assess in practice. Vessel myography is used to determine the functional mechanisms and evaluate pharmacological responses of vascularly-targeted therapeutics. However, myography can only be performed on ex vivo sections of individual arteries. We have developed methods for implementation of spherical-view photoacoustic tomography for non-invasive and in vivo myography. Using photoacoustic tomography, we demonstrate the measurement of acute vascular reactivity in the systemic vasculature and the placenta of female pregnant mice in response to two vasodilators. Photoacoustic tomography simultaneously captures the significant acute vasodilation of major arteries and detects selective vasoactivity of the maternal-fetal vasculature. Photoacoustic tomography has the potential to provide invaluable preclinical information on vascular response that cannot be obtained by other established methods.

Long-term optical imaging of the spinal cord in awake, behaving animals

Advances in optical imaging approaches and fluorescent biosensors have enabled an understanding of the spatiotemporal and long-term neural dynamics in the brain of awake animals. However, methodological difficulties and the persistence of post-laminectomy fibrosis have greatly limited similar advances in the spinal cord. To overcome these technical obstacles, we combined in vivo application of fluoropolymer membranes that inhibit fibrosis; a redesigned, cost-effective implantable spinal imaging chamber; and improved motion correction methods that together permit imaging of the spinal cord in awake, behaving mice, for months to over a year. We also demonstrate a robust ability to monitor axons, identify a spinal cord somatotopic map, conduct Ca2+ imaging of neural dynamics in behaving animals responding to pain-provoking stimuli, and observe persistent microglial changes after nerve injury. The ability to couple neural activity and behavior at the spinal cord level will drive insights not previously possible at a key location for somatosensory transmission to the brain.

Head-to-tail imaging of mice with spiral volumetric optoacoustic tomography

Optoacoustic tomography has been established as a powerful modality for preclinical imaging. However, efficient whole-body imaging coverage has not been achieved owing to the arduous requirement for continuous acoustic coupling around the animal. In this work, we introduce panoramic (360⁰) head-to-tail 3D imaging of mice with spiral volumetric optoacoustic tomography (SVOT). The system combines multi-beam illumination and a dedicated head holder enabling uninterrupted acoustic coupling for whole-body scans. Image fidelity is optimized with self-gated respiratory motion rejection and dual speed-of-sound reconstruction algorithms to attain spatial resolution down to 90 µm. The developed system is thus highly suitable for visualizing rapid biodynamics across scales, such as hemodynamic changes in individual organs, responses to treatments and stimuli, perfusion, total body accumulation, or clearance of molecular agents and drugs with unmatched contrast, spatial and temporal resolution.

Hessian filter-assisted full diameter at half maximum (FDHM) segmentation and quantification method for optical-resolution photoacoustic microscopy

Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.

Frequency wavelength multiplexed optoacoustic tomography

Optoacoustics (OA) is overwhelmingly implemented in the Time Domain (TD) to achieve high signal-to-noise ratios by maximizing the excitation light energy transient. Implementations in the Frequency Domain (FD) have been proposed, but suffer from low signal-to-noise ratios and have not offered competitive advantages over time domain methods to reach high dissemination. It is therefore commonly believed that TD is the optimal way to perform optoacoustics. Here we introduce an optoacoustic concept based on pulse train illumination and frequency domain multiplexing and theoretically demonstrate the superior merits of the approach compared to the time domain. Then, using recent advances in laser diode illumination, we launch Frequency Wavelength Multiplexing Optoacoustic Tomography (FWMOT), at multiple wavelengths, and experimentally showcase how FWMOT optimizes the signal-to-noise ratios of spectral measurements over time-domain methods in phantoms and in vivo. We further find that FWMOT offers the fastest multi-spectral operation ever demonstrated in optoacoustics.

Optoacoustic imaging is mostly performed in the time domain. Here the authors demonstrate frequency wavelength multiplexed optoacoustic tomography that can operate at multiple wavelengths simultaneously and offers signal-to-noise ratio advantages over time domain methods.

Multi-focus image fusion with enhancement filtering for robust vascular quantification using photoacoustic microscopy

Accurate identification and quantification of microvascular patterns are important for clinical diagnosis and therapeutic monitoring using optical-resolution photoacoustic microscopy (OR-PAM). Due to its limited depth of field, conventional OR-PAM may not fully reveal microvascular patterns with enough details in depth range, which affects the segmentation and quantification. Here, we propose a robust vascular quantification approach via combining multi-focus image fusion with enhancement filtering (MIFEF). The multi-focus image fusion is constructed based on multi-scale gradients and image matting to improve image fusion quality by considerably achieving accurate focus measurement for initial segmentation as well as decision map refinement. The enhancement filtering identifies the vessels and handles noise without deforming microvasculature. The performance of the MIFEF were evaluated employing a leaf phantom, mouse livers and brains. The proposed method for OR-PAM can significantly facilitate the clinical provision of optical biopsy of vascular-related diseases.

Automated Image Threshold Method Comparison for Conjunctival Vessel Quantification on Optical Coherence Tomography Angiography

Purpose

To determine the impact of image binarization and the best thresholding method for conjunctival optical coherence tomography angiography (OCTA).

Methods

Vessel density (VD) of 14 OCTA conjunctival images (nine nasal and five temporal conjunctivas, and eight right and six left eyes) from normal subjects was analyzed. The binarization of gold-standard images, created by removing pixels that do not represent vessels on ImageJ software, was assessed by three masked graders to determine consistency of VD for images. Various thresholding methods on ImageJ, including manual, 1-, 2- and 3-step processes, were performed on unprocessed images for comparison. Interclass correlation coefficient (ICC) ≥0.750 were classified as good reliability and selected for calculation of the performance of the pixel location in the binarized images of each method.

Results

Analysis of the gold-standard threshold method achieved an ICC of 0.816 with excellent agreement (R² = 0.965, P < 0.001). From a total 28 different methods and variations performed, only nine methods performed with good reliability, including two 1-step thresholds, six 2-step thresholds, and one 3-step threshold method. Overall, 2-step threshold methods were more reliable than 3-step threshold methods. The 2-step method of Bandpass filter + Phansalkar local threshold (LT) showed the best performance with mean pixel accuracy of 86.9% ± 6.8%, area under the curve of 0.826, sensitivity of 79.0%, and specificity 86.1%.

Conclusions

Bandpass filter + Phansalkar LT was the best method for VD measurement in conjunctival OCTA. Most commonly reported threshold methods showed unsatisfactory agreement. There is a need in the OCTA field for a standardized method to allow comparison between different studies.

Translational Relevance

The proposed threshold method using a widely accessible and commonly used software provides an accurate VD measurement for future OCTA studies.

Trainable Segmentation for Transmission Electron Microscope Images of Inorganic Nanoparticles

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a Random Forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images. This article is protected by copyright. All rights reserved.

Longitudinal in-vivo quantification of tumour microvascular heterogeneity by optical coherence angiography in pre-clinical radiation therapy

Stereotactic body radiotherapy (SBRT) is an emerging cancer treatment due to its logistical and potential therapeutic benefits as compared to conventional radiotherapy. However, its mechanism of action is yet to be fully understood, likely involving the ablation of tumour microvasculature by higher doses per fraction used in SBRT. In this study, we hypothesized that longitudinal imaging and quantification of the vascular architecture may elucidate the relationship between the microvasculature and tumour response kinetics. Pancreatic human tumour xenografts were thus irradiated with single doses of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10$$\end{document}10, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20$$\end{document}20 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$30$$\end{document}30 Gy to simulate the first fraction of a SBRT protocol. Tumour microvascular changes were monitored with optical coherence angiography for up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8$$\end{document}8 weeks following irradiation. The temporal kinetics of two microvascular architectural metrics were studied as a function of time and dose: the diffusion-limited fraction, representing poorly vascularized tissue \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>150$$\end{document}>150 μm from the nearest detected vessel, and the vascular distribution convexity index, a measure of vessel aggregation at short distances. These biological metrics allowed for dose dependent temporal evaluation of tissue (re)vascularization and vessel aggregation after radiotherapy, showing promise for determining the SBRT dose–response relationship.

Sparse Coding-Enabled Low-Fluence Multi-Parametric Photoacoustic Microscopy

Uniquely capable of simultaneous imaging of the hemoglobin concentration, blood oxygenation, and flow speed at the microvascular level in vivo, multi-parametric photoacoustic microscopy (PAM) has shown considerable impact in biomedicine. However, the multi-parametric PAM acquisition requires dense sampling and thus a high laser pulse repetition rate (up to MHz), which sets a strict limit on the applicable pulse energy due to safety considerations. A similar limitation is shared by high-speed PAM, which also uses lasers with high pulse repetition rates. To achieve high quantitative accuracy besides good structural visualization at low levels of laser fluence in PAM, we have developed a new, sparse coding-based two-step denoising technique. In the setting of intravital brain imaging, we demonstrated that this unsupervised learning approach enabled the reduction of the laser fluence in PAM by 5 times without compromise of the image quality (structural similarity index measure or SSIM: >0.92) and the quantitative accuracy (errors: <4.9%). Offering a significant relaxation in the requirement of PAM on laser fluence while maintaining the quality of structural imaging and accuracy of quantitative measurements, this sparse coding-based approach is expected to facilitate the application and clinical translation of multi-parametric PAM and high-speed PAM, which have a tight photon budget due to either safety considerations or laser source limitations.

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

Automatic 3-D segmentation and volumetric light fluence correction for photoacoustic tomography based on optimal 3-D graph search

Preclinical imaging with photoacoustic tomography (PAT) has attracted wide attention in recent years since it is capable of providing molecular contrast with deep imaging depth. The automatic extraction and segmentation of the animal in PAT images is crucial for improving image analysis efficiency and enabling advanced image post-processing, such as light fluence (LF) correction for quantitative PAT imaging. Previous automatic segmentation methods are mostly two-dimensional approaches, which failed to conserve the 3-D surface continuity because the image slices were processed separately. This discontinuity problem further hampers LF correction, which, ideally, should be carried out in 3-D due to spatially diffused illumination. Here, to solve these problems, we propose a volumetric auto-segmentation method for small animal PAT imaging based on the 3-D optimal graph search (3-D GS) algorithm. The 3-D GS algorithm takes into account the relation among image slices by constructing a 3-D node-weighted directed graph, and thus ensures surface continuity. In view of the characteristics of PAT images, we improve the original 3-D GS algorithm on graph construction, solution guidance and cost assignment, such that the accuracy and smoothness of the segmented animal surface were guaranteed. We tested the performance of the proposed method by conducting in vivo nude mice imaging experiments with a commercial preclinical cross-sectional PAT system. The results showed that our method successfully retained the continuous global surface structure of the whole 3-D animal body, as well as smooth local subcutaneous tumor boundaries at different development stages. Moreover, based on the 3-D segmentation result, we were able to simulate volumetric LF distribution of the entire animal body and obtained LF corrected PAT images with enhanced structural visibility and uniform image intensity.

1.7-Micron Optical Coherence Tomography Angiography for Characterization of Skin Lesions-A Feasibility Study

Optical coherence tomography (OCT) is a non-invasive diagnostic method that offers real-time visualization of the layered architecture of the skin in vivo. The 1.7-micron OCT system has been applied in cardiology, gynecology and dermatology, demonstrating an improved penetration depth in contrast to conventional 1.3-micron OCT. To further extend the capability, we developed a 1.7-micron OCT/OCT angiography (OCTA) system that allows for visualization of both morphology and microvasculature in the deeper layers of the skin. Using this imaging system, we imaged human skin with different benign lesions and described the corresponding features of both structure and vasculature. The significantly improved imaging depth and additional functional information suggest that the 1.7-micron OCTA system has great potential to advance both dermatological clinical and research settings for characterization of benign and cancerous skin lesions.

Binary photoacoustic tomography for improved vasculature imaging

SIGNIFICANCE

The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs.

AIM

Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible.

APPROACH

Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes.

RESULTS

Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods.

CONCLUSION

The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.

Developing a normative database for retinal perfusion using optical coherence tomography angiography

Visualizing and characterizing microvascular abnormalities with optical coherence tomography angiography (OCTA) has deepened our understanding of ocular diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Two types of microvascular defects can be detected by OCTA: focal decrease because of localized absence and collapse of retinal capillaries, which is referred to as the non-perfusion area in OCTA, and diffuse perfusion decrease usually detected by comparing with healthy case-control groups. Wider OCTA allows for insights into peripheral retinal vascularity, but the heterogeneous perfusion distribution from the macula, parapapillary area to periphery hurdles the quantitative assessment. A normative database for OCTA could estimate how much individual's data deviate from the normal range, and where the deviations locate. Here, we acquired OCTA images using a swept-source OCT system and a 12×12 mm protocol in healthy subjects. We automatically segmented the large blood vessels with U-Net, corrected for anatomical factors such as the relative position of fovea and disc, and segmented the capillaries by a moving window scheme. A total of 195 eyes were included and divided into 4 age groups: < 30 (n=24) years old, 30-49 (n=28) years old, 50-69 (n=109) years old and >69 (n=34) years old. This provides an age-dependent normative database for characterizing retinal perfusion abnormalities in 12×12 mm OCTA images. The usefulness of the normative database was tested on two pathological groups: one with diabetic retinopathy; the other with glaucoma.

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).