Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the ...processing of 2D images.
The MorphoLibJ library proposes a large collection of generic tools based on MM to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. We illustrate how MorphoLibJ can facilitate the exploitation of 3D images of plant tissues.
MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: david.legland@nantes.inra.frSupplementary information: Supplementary data are available at Bioinformatics online.
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then ...the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.
Dental morphology is a major aspect of ecological and evolutionary studies of both extant and fossil mammalian species. Mammalian dentitions are diverse feeding systems that can be defined through ...continuous numerical descriptors of the enamel pattern.
We developed a comprehensive toolkit to quantify complex occlusal enamel patterns from two‐dimensional images of herbivore mammals, widespread in the scientific literature, in form of three novel enamel complexity descriptors: two‐dimensional orientation patch count (2D OPC), enamel folding (EF), and enamel thickness (ET). Previously proposed parameters such as occlusal enamel index or indentation index are implemented as well.
The current method is devised for extracting continuous variables of enamel complexity from macro and microherbivore mammalian species with conspicuous wear facets. A general case study is proposed using two clades within the Family Rhinocerotidae containing species regarded as hypsodonts. The results show that antagonist dental adaptations were achieved through disparate evolutionary strategies in both groups. To test the robustness of this tool under different practical scenarios, other mammalian groups have been evaluated as well. Additional sensitivity analyses include the impact of image size, rotation, or differences in dental wear.
Our approach differs from previous 2D techniques in its affordability, versatility, and control over individual regions within each tooth while delivering continuous numerical data. Additionally, the 2D reference images required as input are widespread in the literature and easier to process in comparison to 3D data alternatives.
State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This ...process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.
TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation .
ignacio.arganda@ehu.eus.
Supplementary data are available at Bioinformatics online.
Large-scale microscopy approaches are transforming brain imaging, but currently lack efficient multicolor contrast modalities. We introduce chromatic multiphoton serial (ChroMS) microscopy, a method ...integrating one-shot multicolor multiphoton excitation through wavelength mixing and serial block-face image acquisition. This approach provides organ-scale micrometric imaging of spectrally distinct fluorescent proteins and label-free nonlinear signals with constant micrometer-scale resolution and sub-micron channel registration over the entire imaged volume. We demonstrate tridimensional (3D) multicolor imaging over several cubic millimeters as well as brain-wide serial 2D multichannel imaging. We illustrate the strengths of this method through color-based 3D analysis of astrocyte morphology and contacts in the mouse cerebral cortex, tracing of individual pyramidal neurons within densely Brainbow-labeled tissue, and multiplexed whole-brain mapping of axonal projections labeled with spectrally distinct tracers. ChroMS will be an asset for multiscale and system-level studies in neuroscience and beyond.
The control of plant parasitic nematodes is an increasing problem. A key process during the infection is the induction of specialized nourishing cells, called giant cells (GCs), in roots. ...Understanding the function of genes required for GC development is crucial to identify targets for new control strategies. We propose a standardized method for GC phenotyping in different plant genotypes, like those with modified genes essential for GC development. The method combines images obtained by bright‐field microscopy from the complete serial sectioning of galls with TrakEM2, specialized three‐dimensional (3D) reconstruction software for biological structures. The volumes and shapes from 162 3D models of individual GCs induced by Meloidogyne javanica in Arabidopsis were analyzed for the first time along their life cycle. A high correlation between the combined volume of all GCs within a gall and the total area occupied by all the GCs in the section/s where they show maximum expansion, and a proof of concept from two Arabidopsis transgenic lines (J0121 ≫ DTA and J0121 ≫ GFP) demonstrate the reliability of the method. We phenotyped GCs and developed a reliable simplified method based on a two‐dimensional (2D) parameter for comparison of GCs from different Arabidopsis genotypes, which is also applicable to galls from different plant species and in different growing conditions, as thickness/transparency is not a restriction.
To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. ...Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.
With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological ...studies. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists.
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