NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects.

BACKGROUND: The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. RESULTS: We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. CONCLUSION AND AVAILABILITY: NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE .

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.

Automated 3D bio-imaging analysis of nuclear organization by NucleusJ 2.0.

NucleusJ 1.0, an ImageJ plugin, is a useful tool to analyze nuclear morphology and chromatin organization in plant and animal cells. NucleusJ 2.0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-lineuser interface. Starting with large collection of 3D nuclei, segmentation can be performed by the previously developed Otsu-modified method or by a new 3D gift-wrapping method, taking better account of nuclear indentations and unstained nucleoli. These two complementary methods are compared for their accuracy by using three types of datasets available to the community at https://www.brookes.ac.uk/indepth/images/ . Finally, NucleusJ 2.0 was evaluated using original plant genetic material by assessing its efficiency on nuclei stained with DNA dyes or after 3D-DNA Fluorescence in situ hybridization. With these improvements, NucleusJ 2.0 permits the generation of large user-curated datasets that will be useful for software benchmarking or to train convolution neural networks.

Fast simulation of stent deployment with plastic beam elements.

Coronary stent deployment is a reference cardiology intervention, used to treat atherosclerosis and prevent heart attacks. The outcomes of the intervention highly depend on the accuracy of the stent apposition, which could benefit from per-operative prediction tools. In this paper, we propose a fast and mechanically realistic 3D simulation of a coronary stent expansion. Our simulation relies on the finite element method and involves serially linked beam elements to model the slender geometry of a stent. The elements are implemented with a non-linear elasto-plastic behavior, describing realistically the complex deformation of a balloon-expandable stent. As a proof of concept, we simulated the free expansion of a coronary stent. The simulation output was compared with micro-CT data, acquired experimentally during the device expansion. Results show that the plastic beam model is able to reproduce successfully the final geometry of the stent. In addition, the use of 1D elements allows to achieve a significantly lower computational time than for equivalent literature simulations, based on 3D elements. This preliminary work highlights the compatibility of our method with clinical routine in terms of execution time. Further developments include the application of the method to more advanced simulation scenarios, with the addition of a personalized artery model.

Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions.

PURPOSE: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. METHODS: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. RESULTS: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98). CONCLUSION: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

Automated peroperative assessment of stents apposition from OCT pullbacks.

This study's aim was to control the stents apposition by automatically analyzing endovascular optical coherence tomography (OCT) sequences. Lumen is detected using threshold, morphological and gradient operators to run a Dijkstra algorithm. Wrong detection tagged by the user and caused by bifurcation, struts'presence, thrombotic lesions or dissections can be corrected using a morphing algorithm. Struts are also segmented by computing symmetrical and morphological operators. Euclidian distance between detected struts and wall artery initializes a stent's complete distance map and missing data are interpolated with thin-plate spline functions. Rejection of detected outliers, regularization of parameters by generalized cross-validation and using the one-side cyclic property of the map also optimize accuracy. Several indices computed from the map provide quantitative values of malapposition. Algorithm was run on four in-vivo OCT sequences including different incomplete stent apposition's cases. Comparison with manual expert measurements validates the segmentation׳s accuracy and shows an almost perfect concordance of automated results.

Spectral features selection and classification for bimodal optical spectroscopy applied to bladder cancer in vivo diagnosis.

This paper describes an experimental study combining spatially resolved autofluorescence (AF) and diffuse reflectance (DR) fibred spectroscopies to discriminate in vivo between healthy and pathological tissues in a preclinical model of bladder cancer. Then, a detailed step-by-step analysis scheme is presented for the extraction and the selection of discriminative spectral features (correlation, linear discriminant, and logistic regression analysis), and for the spectroscopic data final classification algorithms (regularized discriminant analysis and support vector machines). Significant differences between healthy, inflammatory, and tumoral tissues were obtained by selecting a reasonable number of discriminant spectral features from AF, DR, and intrinsic fluorescence spectra, leading to improved sensitivity (87%) and specificity (77%) compared to monomodality (AF or DR alone).

[Physical principles of optical biopsy]. / Principes physiques de la biopsie optique.

The purpose of this presentation is to explain the physical principles underlying the three main methods used to obtain images of living tissues at the cellular scale. In confocal microscopy, the tissue of interest is illuminated and scanned through a confocal aperture; a lateral resolution close to 1 microm can be obtained with high numerical aperture. Multiphoton microscopy uses a high-power short-pulse laser with instantaneous irradiance sufficient to excite fluorescence in a very small focal volume. The concentration of natural tissue fluorophores is too low to obtain an adequate signal, so exogenous fluorophores have to be added, either locally or through the body. These fluorophores can be conjugated to a variety of biomolecules that target specific disease processes, thereby increasing diagnostic specificity. Finally, OCT (optical coherence tomography) provides high-resolution images of entire tissue volumes by using a broadband source and an interferometric configuration; the depth of field and lateral resolution are both on the micrometer scale. These methods allow images to be obtained at the cellular level, but image contrast and stability require specific adjustment for their significance to be established with respect to conventional biopsy methods.

Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation.

This work is first a description of a statistical simulation algorithm developed for simulating the spectral absorption and emission of several fluorophores in an absorbing and diffusing multilayer model. Second, a detailed experimental validation of the simulation program is conducted on two sets of liquid and solid multilayer phantoms, containing one, two, or three fluorophores, within absorbing and scattering media. Experimental spatially resolved reflectance spectra are acquired in the wavelength band 400 to 800 nm and compared to corresponding simulated spectra. The degree of similarity between experimentation and simulation data is quantified. The results obtained underline good correlations with mean errors varying from 2 to 10%, depending on the number of layers and on the complexity of the phantom's composition.

Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: experimental and simulation results.

This study aimed at identifying potential correlations between rheological and optical properties of carotid artery rings before and after cryopreservation at different mechanical deformations using experimental and simulation results. Therefore, a uniaxial mechanical test bench was coupled to fibered optical spectroscopes measuring 410 nm excited autofluorescence and 650-850 nm elastically backscattered intensity spectra. Furthermore, we developed a statistical simulation program of light transport and fluorescence adapted to our specific experimental configuration. Both spectroscopies gave intensity spectra with higher amplitude for the cryopreserved samples. These observations are to be related to histological modifications affecting the arterial wall of postcryopreserved samples. We also observed significant spectral amplitude variations (increasing autofluorescence intensity and decreasing diffuse reflectance) as a function of the circumferential strains (0%-60%). Due to simulation, we identified values of absorption, diffusion, and anisotropy coefficients, and their variations as a function of state (fresh-cryopreserved), strains (0, 30%, 60%), and wavelengths (700, 740, 780 nm). The media and the adventice are, respectively, less and more absorbing for postcryopreserved rings, and it is the opposite for the fresh ones at higher wavelengths. Absorption and diffusion coefficients are slightly higher, whatever the wavelengths and strains, for the fresh than for the cryopreserved samples.

Experimental comparison between autofluorescence spectra of constrained fresh and cryopreserved arteries.

The study of mechanical properties of the arterial wall is an important step in the comprehension of the vascular physiopathological functioning. However, cryopreserving biological tissues using very low temperatures can induce biological and structural modifications which may involve complications (dilatation, bursting, stenosis) after reimplantation. Many procedures of mechanical tests (traction, dilatation) developed in research allow us to comprehend and analyse rheological behaviour of the arterial wall. The study presented in this article offers a new perspective to detect changes of mechanical properties of cryopreserved arterial samples. In fact, the original idea is to couple a mechanical test bed (uniaxial traction of arterial rings) with spectroscopic measurements (autofluorescence) for the purpose of correlating mechanical modifications and spectral variations. Ultimately, this new approach could lead to develop a device allowing atraumatic and contactless optical examinations of arterial graft to determine its mechanical state before reimplantation.