Neuron Segmentation Using Deep Complete Bipartite Networks

In this paper, we consider the problem of automatically segmenting neuronal cells in dual-color confocal microscopy images. This problem is a key task in various quantitative analysis applications in neuroscience, such as tracing cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using fully convolutional networks (FCN), has profoundly changed segmentation research in biomedical imaging. We face two major challenges in this problem. First, neuronal cells may form dense clusters, making it difficult to correctly identify all individual cells (even to human experts). Consequently, segmentation results of the known FCN-type models are not accurate enough. Second, pixel-wise ground truth is difficult to obtain. Only a limited amount of approximate instance-wise annotation can be collected, which makes the training of FCN models quite cumbersome. We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model. Evaluated using seven real datasets, our proposed new CB-Net model outperforms the state-of-the-art FCN models and produces neuron segmentation results of remarkable qualityComment: miccai 201

PhOTO Zebrafish: A Transgenic Resource for In Vivo Lineage Tracing during Development and Regeneration

Background: Elucidating the complex cell dynamics (divisions, movement, morphological changes, etc.) underlying embryonic development and adult tissue regeneration requires an efficient means to track cells with high fidelity in space and time. To satisfy this criterion, we developed a transgenic zebrafish line, called PhOTO, that allows photoconvertible optical tracking of nuclear and membrane dynamics in vivo. Methodology: PhOTO zebrafish ubiquitously express targeted blue fluorescent protein (FP) Cerulean and photoconvertible FP Dendra2 fusions, allowing for instantaneous, precise targeting and tracking of any number of cells using Dendra2 photoconversion while simultaneously monitoring global cell behavior and morphology. Expression persists through adulthood, making the PhOTO zebrafish an excellent tool for studying tissue regeneration: after tail fin amputation and photoconversion of a ~100µm stripe along the cut area, marked differences seen in how cells contribute to the new tissue give detailed insight into the dynamic process of regeneration. Photoconverted cells that contributed to the regenerate were separated into three distinct populations corresponding to the extent of cell division 7 days after amputation, and a subset of cells that divided the least were organized into an evenly spaced, linear orientation along the length of the newly regenerating fin. Conclusions/Significance: PhOTO zebrafish have wide applicability for lineage tracing at the systems-level in the early embryo as well as in the adult, making them ideal candidate tools for future research in development, traumatic injury and regeneration, cancer progression, and stem cell behavior

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain

Published: 23 March 2018The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fninf.2018.00013/full#supplementary-material Supplementary Figure 1. 3D renderings of the 14 regions used for quantitative evaluation of atlas performances in segmentation and registration tasks. The 14 regions shown here were extracted from the atlas of Ito et al. (2014) that has been registered onto the group-wise inter-sex atlas (available from http://fruitfly.tefor.net). Supplementary Figure 2. Selected lines from the Janelia Farm collection showing an overlap value with the search pattern ranking among the first 50 for at least three of the five PDF profiles. (Left) GAL4-driven GFP profile registered on the standard brain. (Right) overlap between the first PDF profile and the GAL4-driven GFP profile. Numbers refer to Janelia Farm lines with associated gene names. Scale bar: 20 μm. Supplementary Table 1. Results of the 3D space query for each of the five PDF profiles. Overlap values are indicated for each Janelia Farm line and the corresponding gene name (FlyBase nomenclature) is indicated for the overlap values ranking among the first 50 for at least three of the five PDF profiles (blue). Bold names correspond to the three lines shown in Figure 10. Supplementary Movie 1. Animated rendering of the group-wise inter-sex atlas. Successively: nc82 template image (2D sections then 3D volume rendering, opaque then transparent); label image (3D surface rendering of anatomical regions, defined following Ito et al. 2014); six registered patterns of GAL4-GFP expression (3D surface rendering of intensity-thresholded pattern images); same patterns (left half of the brain) with the anatomical regions (right half of the brain).Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.IA-C, TM, NM, FS, and AJ were funded by the Tefor Infrastructure under the Investments for the Future program of the French National Research Agency (Grant #ANR-11-INBS-0014). FR was supported by INSERM. Work at Institut des Neurosciences Paris-Saclay was supported by ANR Infrastructure Tefor and by ANR ClockEye(#ANR-14-CE13-0034-01). JI was supported by the Spanish Ministry of Economy and Competitiveness (TEC2014-51882-P), the European Union's Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 654911, project THALAMODEL), and the European Research Council (ERC Starting Grant no. 677697 BUNGEE-TOOLS). VRVis (KB, FS) is funded by BMVIT, BMWFW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174) which is managed by FFG. The Institut Jean-Pierre Bourgin benefits from the support of the LabEx Saclay Plant Sciences-SPS (#ANR-10-LABX-0040-SPS)

Registration and Analysis of Developmental Image Sequences

Mapping images into the same anatomical coordinate system via image registration is a fundamental step when studying physiological processes, such as brain development. Standard registration methods are applicable when biological structures are mapped to the same anatomy and their appearance remains constant across the images or changes spatially uniformly. However, image sequences of animal or human development often do not follow these assumptions, and thus standard registration methods are unsuited for their analysis. In response, this dissertation tackles the problems of i) registering developmental image sequences with spatially non-uniform appearance change and ii) reconstructing a coherent 3D volume from serially sectioned images with non-matching anatomies between the sections. There are three major contributions presented in this dissertation. First, I develop a similarity metric that incorporates a time-dependent appearance model into the registration framework. The proposed metric allows for longitudinal image registration in the presence of spatially non-uniform appearance change over time—a common medical imaging problem for longitudinal magnetic resonance images of the neonatal brain. Next, a method is introduced for registering longitudinal developmental datasets with missing time points using an appearance atlas built from a population. The proposed method is applied to a longitudinal study of young macaque monkeys with incomplete image sequences. The final contribution is a template-free registration method to reconstruct images of serially sectioned biological samples into a coherent 3D volume. The method is applied to confocal fluorescence microscopy images of serially sectioned embryonic mouse brains.Doctor of Philosoph

Visualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ducts

Aiming at a better result from previous works, we employed some heuristics found in the literature to determine the appropriate parameters for the clustering. We proposed our methodology by adding some steps to be performed before the clustering phase: one step for pre-processing the volumetric dataset and another to analyzing candidate features to guide the clustering. In this latter aspect, we provide an interesting contribution: we have explored the gradient magnitude as a feature that allowed to extract relevant information from the density-based spatial clustering. Besides the fact that DBSCAN allows easy detection of noise points, an interesting result for both datasets was that the first and largest cluster found as significant for the visualization represents the structure of interest. In the red channel, this cluster represents the most prominent vessels, while in the green channel, the peribiliary glands were made more evident.Abstract—Three-dimensional datasets from biological tissues have increased with the evolution of confocal microscopy. Hepatology researchers have used confocal microscopy for investigating the microanatomy of bile ducts. Bile ducts are complex tubular tissues consisting of many juxtaposed microstructures with distinct characteristics. Since confocal images are difficult to segment because of the noise introduced during the specimen preparation, traditional quantitative analyses used in medical datasets are difficult to perform on confocal microscopy data and require extensive user intervention. Thus, the visual exploration and analysis of bile ducts pose a challenge in hepatology research, requiring different methods. This paper investigates the application of unsupervised machine learning to extract relevant structures from confocal microscopy datasets representing bile ducts. Our approach consists of pre-processing, clustering, and 3D visualization. For clustering, we explore the density-based spatial clustering for applications with noise (DBSCAN) algorithm, using gradient information for guiding the clustering. We obtained a better visualization of the most prominent vessels and internal structures.info:eu-repo/semantics/publishedVersio