scholarly journals Assessment of deep learning algorithms for 3D instance segmentation of confocal image datasets

2021 ◽  
Author(s):  
Anuradha Kar ◽  
Manuel Petit ◽  
Yassin Refahi ◽  
Guillaume Cerutti ◽  
Christophe Godin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Cellular Growth ◽  
Evaluation Strategies ◽  
Segmentation Quality ◽  
Image Artefacts ◽  
Microscopy Images ◽  
Different Levels

Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performance as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artefacts. A new method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with new visualisation strategies that make interactive exploration of segmentation quality possible. Our analysis shows that the DL pipelines have very different levels of accuracy. Two of them show high performance, and offer clear advantages in terms of adaptability to new data.

An advanced geometrical model for laminated woven fabrics using Lamé exponents with enhanced accuracy

2017 ◽  
Vol 52 (11) ◽  
pp. 1443-1455
Author(s):  
Mike Mühlstädt ◽  
Wolfgang Seifert ◽  
Matthias ML Arras ◽  
Stefan Maenz ◽  
Klaus D Jandt ◽  
...  
Keyword(s):  
High Performance ◽  
Volume Fraction ◽  
Level Structure ◽  
Three Dimensional ◽  
Geometrical Model ◽  
Woven Fabrics ◽  
Fiber Volume ◽  
Meso Level ◽  
Precise Prediction ◽  
Microscopy Images

Three-dimensional stiffness tensors of laminated woven fabrics used in high-performance composites need precise prediction. To enhance the accuracy in three-dimensional stiffness tensor prediction, the fabric’s architecture must be precisely modeled. We tested the hypotheses that: (i) an advanced geometrical model describes the meso-level structure of different fabrics with a precision higher than established models, (ii) the deviation between predicted and experimentally determined mean fiber-volume fraction ( cf) of laminates is below 5%. Laminates of different cf and fabrics were manufactured by resin transfer molding. The laminates’ meso-level structure was determined by analyzing scanning electron microscopy images. The prediction of the laminates’ cf was improved by up to 5.1 vol% ([Formula: see text]%) compared to established models. The effect of the advanced geometrical model on the prediction of the laminate’s in-plane stiffness was shown by applying a simple mechanical model. Applying an advanced geometrical model may lead to more accurate simulations of parts for example in automotive and aircraft.

scholarly journals Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

2019 ◽  
Vol 16 (12) ◽  
pp. 1323-1331 ◽  
Author(s):  
Yichen Wu ◽  
Yair Rivenson ◽  
Hongda Wang ◽  
Yilin Luo ◽  
Eyal Ben-David ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Three Dimensional ◽  
Microscopy Images

scholarly journals DeepNeuron: An Open Deep Learning Toolbox for Neuron Tracing

2018 ◽  
Author(s):  
Zhi Zhou ◽  
Hsien-Chi Kuo ◽  
Hanchuan Peng ◽  
Fuhui Long
Keyword(s):  
Deep Learning ◽  
Light Microscopy ◽  
Three Dimensional ◽  
Brain Structures ◽  
Learning Networks ◽  
Neuron Morphology ◽  
Neuron Tracing ◽  
Wide Range ◽  
Confocal Images ◽  
Microscopy Images

AbstractReconstructing three-dimensional (3D) morphology of neurons is essential to understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semi-automatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new open source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

scholarly journals A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule

2020 ◽  
Author(s):  
Athul Vijayan ◽  
Rachele Tofanelli ◽  
Soeren Strauss ◽  
Lorenzo Cerrone ◽  
Adrian Wolny ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Growth Patterns ◽  
Cellular Growth ◽  
Ovule Development ◽  
Fluorescent Reporter ◽  
Control Of Gene Expression ◽  
Molecular Control ◽  
Specific Analysis

AbstractA fundamental question in biology is how morphogenesis integrates the multitude of distinct processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Investigating morphogenesis of complex organs strongly benefits from three-dimensional representations of the organ under study. Here, we present a digital analysis of ovule development from Arabidopsis thaliana as a paradigm for a complex morphogenetic process. Using machine-learning-based image analysis we generated a three-dimensional atlas of ovule development with cellular resolution. It allows quantitative stage- and tissue-specific analysis of cellular patterns. Exploiting a fluorescent reporter enabled precise spatial determination of gene expression patterns, revealing subepidermal expression of WUSCHEL. Underlying the power of our approach, we found that primordium outgrowth progresses evenly, discovered a novel mode of forming a new cell layer, and detected a new function of INNER NO OUTER in restricting cell proliferation in the nucellus. Moreover, we identified two distinct subepidermal cell populations that make crucial contributions to ovule curvature. Our work demonstrates the expedience of a three-dimensional digital representation when studying the morphogenesis of an organ of complex cellular architecture and shape that eventually consists of 1,900 cells.

scholarly journals A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Athul Vijayan ◽  
Rachele Tofanelli ◽  
Sören Strauss ◽  
Lorenzo Cerrone ◽  
Adrian Wolny ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Temporal Analysis ◽  
Growth Patterns ◽  
Cellular Growth ◽  
Nucellar Cell ◽  
Control Of Gene Expression ◽  
Molecular Control ◽  
Spatio Temporal

A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana, enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.

Improved 3-D reconstruction using stereo computer graphics and multiple tilt EM images

1995 ◽  
Vol 53 ◽  
pp. 630-631
Author(s):  
Lee D. Peachey ◽  
Lou Fodor ◽  
John C. Haselgrove ◽  
Stanley M. Dunn ◽  
Junqing Huang
Keyword(s):  
Computer Graphics ◽  
High Performance ◽  
Transverse Section ◽  
Three Dimensional ◽  
Stereo Pair ◽  
3D Reconstructions ◽  
Video Cameras ◽  
Biological Objects ◽  
Microscope Images ◽  
High Performance Computer

Stereo pairs of electron microscope images provide valuable visual impressions of the three-dimensional nature of specimens, including biological objects. Beyond this one seeks quantitatively accurate models and measurements of the three dimensional positions and sizes of structures in the specimen. In our laboratory, we have sought to combine high resolution video cameras with high performance computer graphics systems to improve both the ease of building 3D reconstructions and the accuracy of 3D measurements, by using multiple tilt images of the same specimen tilted over a wider range of angles than can be viewed stereoscopically. Ultimately we also wish to automate the reconstruction and measurement process, and have initiated work in that direction.Figure 1 is a stereo pair of 400 kV images from a 1 micrometer thick transverse section of frog skeletal muscle stained with the Golgi stain. This stain selectively increases the density of the transverse tubular network in these muscle cells, and it is this network that we reconstruct in this example.

Use Of Tai Chi Elements To Increase Respiratory Amplitude

2019 ◽  
pp. 11-20
Author(s):  
Mihai-Alexandru Citea ◽  
Marius Neculaes
Keyword(s):  
High Performance ◽  
Tai Chi ◽  
Sports Club ◽  
Optimal Functioning ◽  
Physiological Adaptations ◽  
Competitive Activity ◽  
Abdominal Breathing ◽  
Study Participants ◽  
Different Levels

High performance sport has a major impact on the physiological adaptations of the respiratory system. The importance of the optimal functioning of this system is essential to achieve top results in high performance sport but also in maintaining a long term health status. Science journals present numerous studies that highlight the benefits of practicing Tai Chi on the general population, with effects ranging from improving cardiac function, to influencing the immune system. The purpose of this study is to identify whether by practicing Tai Chi forms a athlete can change their breathing pattern and develop their respiratory amplitude. The subjects of the study were 22 fencing practitioners, accredited at the Iași Municipal Sports Club (C.S.M. Iași), aged between 14 and 18 years, with over 3 years of competitive activity. Materials and method: The study participants were evaluated initially and at the end of 7 months of practice. The frequency was 3 sessions per week, and the duration of each session was 20-30 minutes. The evaluation consisted in measuring the circumference of the thorax at 3 different levels: subaxillary, medial thorax (T6-T7) and lower rib (diaphragmatic) in maximal inspiration and expiration. Conclusions: A constant evolution is observed in most of the exposed cases. In cases where this evolution is not visible, a change in the breathing mode can be noticed, transforming from an upper rib breathing into a thoracic or abdominal breathing. With the exception of one case, all subjects had an improvement of the value in the lower rib level.

Sign in / Sign up

Export Citation Format

Share Document