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.

Common (Cystic) Lymphatic Malformations: Current Knowledge and Management.

The approach to treating common (cystic) lymphatic malformations (LMs) has evolved significantly over the last decade due to clinical research and recent developments in molecular biology. Surgery, sclerosing agents, and medical drugs with specific targets for biological therapy have been reported for the management of LMs. We will discuss the importance to standardize the location and imaging characterization of LMs to improve the knowledge about the outcome of the different therapeutic options. Our goal is to help the reader understand the different options for the management of LMs with the balance between risk and benefit for the patients.

Imaging findings in epithelioid hemangioendothelioma.

PURPOSE-OBJECTIVE: Epithelioid hemangioendothelioma (EHE) is a rare vascular malignancy with varying biologic behavior. The purpose of this study was to identify imaging findings most characteristic of EHE. METHODS: Retrospective review of clinical and imaging records in patients referred to our Vascular Anomalies Center over a 17 year period with biopsy proven EHE. RESULTS: We evaluated 29 patients (17 F) with median age of 16 years (range 2-76 y). The most common presenting symptoms were pain (n = 13) and palpable mass (n = 7). 22 (70%) had multifocal disease. Most common sites of involvement were lung (n = 25), liver (n = 16), bone (n = 12), soft tissue (n = 3) and lymph nodes (n = 1). Of patients with single site disease, 3 had lung, 3 liver, and 1 had bone lesions. In 18/25 with lung disease, there were multiple nodules of varying sizes and characteristics. In 14/16 with hepatic disease there were multiple nodules with predominantly peripheral distribution. Subcapsular retraction was seen in 10/16 and a "lollipop" sign (hepatic or portal vein tapering at the edge of a well-defined hypoenhancing lesion) identified in 5/16. Of 12 osseous lesions, 11 were lytic, 8 involved vertebrae and 9 involved the axial skeleton. CONCLUSION: EHE has varied imaging findings. The most common sites are lungs, liver, and bone, with multi-organ involvement seen in most. Lung disease is most commonly characterized by multiple nodules. Hepatic lesions demonstrate the most distinctive findings, with peripheral distribution, lack of early enhancement, subcapsular retraction and "lollipop" sign. Osseous lesions are commonly lytic and more prevalent in the axial skeleton.

Effectiveness and Safety of Sclerotherapy for Treatment of Low-Flow Vascular Malformations of the Oropharyngeal Region.

PURPOSE: To evaluate the efficacy and safety of sclerotherapy with sodium tetradecyl sulfate (STS) and bleomycin for treatment of venous malformations (VMs) of the oropharyngeal region. MATERIALS AND METHODS: A retrospective study of 33 patients with 46 VMs of the buccal and pharyngolaryngeal cavity associated with impairment of eating, respiration, or elocution was performed. Individual lesions were divided based on their anterior or posterior location, using the base of the tongue as an anatomic landmark. Lesion size was estimated with the use of orthogonal measurements on magnetic resonance or ultrasound images before and after treatment to assess radiologic response. Sclerotherapy sessions were performed under ultrasound, fluoroscopic, and, if needed, endoscopic guidance. Clinical response was assessed with the use of the Manchester Orofacial Pain Disability Scale. Methods for airway management were also compiled. RESULTS: Following sclerotherapy, average VM diameter was reduced by 31.4% (P < .0001) on a per-patient basis and by 30.8% (P < .0001) on a per-lesion basis. The Manchester score improved by an average of 37.0% (P = .013). Four patients reported a worsening of symptoms, and 11 patients experienced symptomatic recurrence. Complications include pneumonia (5 patients) and urgent placement of a post-procedure tracheostomy (4 patients). Patients with posterior malformations experienced more complications (emergency tracheostomies in 4 and pneumonias in 4). CONCLUSIONS: Sclerotherapy using STS is an efficient treatment for venous malformations of the buccal and pharyngolaryngeal cavity but can lead to significant complication for posterior lesions. Careful assessment of the airway is needed before treatment, and prophylactic tracheotomy should be considered in patients with posterior lesions.

Mutual cineMR/RT3DUS cardiac segmentation.

We present a new method to segment a cardiac RT3D ultrasound volume by integrating the registered segmentation of a cardiac cine-MR series in short axis of the same patient. The motivation behind our method is to improve the ultrasound segmentation process by integrating a reference shape built using the cine-MR segmentation on the same patient. As a side effect we obtain a close registration of the cine MR short axis slices with respect to the ultrasound volume. We use the level set framework with a functional including a region-based and a shape-based term. The reference shape is iteratively registered onto the contour during the ultrasound segmentation process and using an affine transform. The proposed method is demonstrated on the MICCAI11 Motion Tracking Challenge database.