Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.
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ABSTRACT:
SUBMITTER: Cao J
PROVIDER: S-EPMC7721714 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
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Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.
Cao Jianfeng J Guan Guoye G Ho Vincy Wing Sze VWS Wong Ming-Kin MK Chan Lu-Yan LY Tang Chao C Zhao Zhongying Z Yan Hong H
Nature communications 20201207 1
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell morphology during development has not yet been systematically characterized in any metazoan, including C. elegans. This knowledge gap substantially hampers many studies in both developmental and cell biology. Here we report an automatic pipeline, CS ...[more]
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Project description:BackgroundUnderstanding the cellular architecture is a fundamental problem in various biological studies. C. elegans is widely used as a model organism in these studies because of its unique fate determinations. In recent years, researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. Although various algorithms have been proposed to analyze nucleus, cell shape features are not yet well recorded. This paper proposes a method to systematically analyze three-dimensional morphological cellular features.ResultsThree-dimensional Membrane Morphological Segmentation (3DMMS) makes use of several novel techniques, such as statistical intensity normalization, and region filters, to pre-process the cell images. We then segment membrane stacks based on watershed algorithms. 3DMMS achieves high robustness and precision over different time points (development stages). It is compared with two state-of-the-art algorithms, RACE and BCOMS. Quantitative analysis shows 3DMMS performs best with the average Dice ratio of 97.7% at six time points. In addition, 3DMMS also provides time series of internal and external shape features of C. elegans.ConclusionWe have developed the 3DMMS based technique for embryonic shape reconstruction at the single-cell level. With cells accurately segmented, 3DMMS makes it possible to study cellular shapes and bridge morphological features and biological expression in embryo research.
| S-EPMC6454620 | biostudies-literature
Project description:BackgroundSegmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.PurposeTo develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI.Study typeRetrospective.SubjectsA total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets.Field strength/sequenceA 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI).AssessmentA 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations.Statistical testsDice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters.ResultsThe following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively.Data conclusionThe addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting.Evidence level4 TECHNICAL EFFICACY: Stage 1.
| S-EPMC10946960 | biostudies-literature
Project description:The nematode Caenorhabditis elegans possesses a simple embryonic nervous system with few enough neurons that the growth of each cell could be followed to provide a systems-level view of development. However, studies of single cell development have largely been conducted in fixed or pre-twitching live embryos, because of technical difficulties associated with embryo movement in late embryogenesis. We present open-source untwisting and annotation software (http://mipav.cit.nih.gov/plugin_jws/mipav_worm_plugin.php) that allows the investigation of neurodevelopmental events in late embryogenesis and apply it to track the 3D positions of seam cell nuclei, neurons, and neurites in multiple elongating embryos. We also provide a tutorial describing how to use the software (Supplementary file 1) and a detailed description of the untwisting algorithm (Appendix). The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an 'average' worm embryo. The untwisting and cell tracking capabilities of our method provide a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis.
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Project description:In the Caenorhabditis elegans nematode, the oocyte nucleolus disappears prior to fertilization. We have now investigated the re-formation of the nucleolus in the early embryo of this model organism by immunostaining for fibrillarin and DAO-5, a putative NOLC1/Nopp140 homolog involved in ribosome assembly. We find that labeled nucleoli first appear in somatic cells at around the 8-cell stage, at a time when transcription of the embryonic genome begins. Quantitative analysis of radial positioning showed the nucleolus to be localized at the nuclear periphery in a majority of early embryonic nuclei. At the ultrastructural level, the embryonic nucleolus appears to be composed of a relatively homogenous core surrounded by a crescent-shaped granular structure. Prior to embryonic genome activation, fibrillarin and DAO-5 staining is seen in numerous small nucleoplasmic foci. This staining pattern persists in the germline up to the ∼100-cell stage, until the P4 germ cell divides to give rise to the Z2/Z3 primordial germ cells and embryonic transcription is activated in this lineage. In the ncl-1 mutant, which is characterized by increased transcription of rDNA, DAO-5-labeled nucleoli are already present at the 2-cell stage. Our results suggest a link between the activation of transcription and the initial formation of nucleoli in the C. elegans embryo.
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Project description:Establishing how neural function emerges from network properties is a fundamental problem in neuroscience1. Here, to better understand the relationship between the structure and the function of a nervous system, we systematically measure signal propagation in 23,433 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from model predictions that are based on anatomy. Using mutants, we show that extrasynaptic signalling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle-dependent signalling, including on timescales of less than a second, that evoke acute calcium transients-often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that, in such cases, extrasynaptically released neuropeptides serve a similar function to that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts the neural dynamics of spontaneous activity than do models based on anatomy. We conclude that both synaptic and extrasynaptic signalling drive neural dynamics on short timescales, and that measurements of evoked signal propagation are crucial for interpreting neural function.
| S-EPMC10632145 | biostudies-literature
Project description:One goal of modern day neuroscience is the establishment of molecular maps that assign unique features to individual neuron types. Such maps provide important starting points for neuron classification, for functional analysis, and for developmental studies aimed at defining the molecular mechanisms of neuron identity acquisition and neuron identity diversification. In this resource paper, we describe a nervous system-wide map of the potential expression sites of 244 members of the largest gene family in the C. elegans genome, rhodopsin-like (class A) G-protein-coupled receptor (GPCR) chemoreceptors, using classic gfp reporter gene technology. We cover representatives of all sequence families of chemoreceptor GPCRs, some of which were previously entirely uncharacterized. Most reporters are expressed in a very restricted number of cells, often just in single cells. We assign GPCR reporter expression to all but two of the 37 sensory neuron classes of the sex-shared, core nervous system. Some sensory neurons express a very small number of receptors, while others, particularly nociceptive neurons, coexpress several dozen GPCR reporter genes. GPCR reporters are also expressed in a wide range of inter- and motorneurons, as well as non-neuronal cells, suggesting that GPCRs may constitute receptors not just for environmental signals, but also for internal cues. We observe only one notable, frequent association of coexpression patterns, namely in one nociceptive amphid (ASH) and two nociceptive phasmid sensory neurons (PHA, PHB). We identified GPCRs with sexually dimorphic expression and several GPCR reporters that are expressed in a left/right asymmetric manner. We identified a substantial degree of GPCR expression plasticity; particularly in the context of the environmentally-induced dauer diapause stage when one third of all tested GPCRs alter the cellular specificity of their expression within and outside the nervous system. Intriguingly, in a number of cases, the dauer-specific alterations of GPCR reporter expression in specific neuron classes are maintained during postdauer life and in some case new patterns are induced post-dauer, demonstrating that GPCR gene expression may serve as traits of life history. Taken together, our resource provides an entry point for functional studies and also offers a host of molecular markers for studying molecular patterning and plasticity of the nervous system.
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Project description:A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.
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Project description:PurposeTo generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions.MethodsA total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland-Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40.ResultsConvolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930-0.966), Hausdorff distance of 2.80 (2.13-4.35), and average symmetrical surface distance of 0.176 (0.119-0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network-based analysis (median Dice score: 0.953-0.959; Hausdorff distance: 2.24-2.91; and average symmetrical surface distance: 0.145-1.98 to observers) demonstrated similar reproducibility.ConclusionsDeep learning enabled fast and automated 3D aortic segmentation from 4D-flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.
| S-EPMC7724647 | biostudies-literature
Project description:Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.
| S-EPMC3732273 | biostudies-literature
Project description:Highlights • Cardiotoxicity is a common complication of lung cancer radiotherapy.• Segmentation of cardiac substructures is time-consuming and challenging.• Deep learning segmentation tools can perform this task in 3D and 4D scans.• Performance is high when assessed geometrically, dosimetrically and clinically.• Auto-segmentation tools may accelerate clinical workflows and enable research. Background Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. Materials and Methods The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool’s output. Results Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing. Conclusions Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.
| S-EPMC9356270 | biostudies-literature