Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive imaging technique allowing to estimate the molecular self-diffusion tensors of water within surrounding tissue. Due to the low ...signal-to-noise ratio of magnetic resonance images, reconstructed tensor images usually require some sort of regularization in a post-processing step. Previous approaches are either suboptimal with respect to the reconstruction or regularization step. This paper presents a Bayesian approach for simultaneous reconstruction and regularization of DT-MR images that allows to resolve the disadvantages of previous approaches. To this end, estimation theoretical concepts are generalized to tensor valued images that are considered as Riemannian manifolds. Doing so allows us to derive a maximum a posteriori estimator of the tensor image that considers both the statistical characteristics of the Rician noise occurring in MR images as well as the nonlinear structure of tensor valued images. Experiments on synthetic data as well as real DT-MRI data validate the advantage of considering both statistical as well as geometrical characteristics of DT-MRI.
Precise measurements of leaf vein traits are an important aspect of plant phenotyping for ecological and genetic research. Here, we present a powerful and user-friendly image analysis tool named ...phenoVein. It is dedicated to automated segmenting and analyzing of leaf veins in images acquired with different imaging modalities (microscope, macrophotography, etc.), including options for comfortable manual correction. Advanced image filtering emphasizes veins from the background and compensates for local brightness inhomogeneities. The most important traits being calculated are total vein length, vein density, piecewise vein lengths and widths, areole area, and skeleton graph statistics, like the number of branching or ending points. For the determination of vein widths, a model-based vein edge estimation approach has been implemented. Validation was performed for the measurement of vein length, vein width, and vein density of Arabidopsis (Arabidopsis thaliana), proving the reliability of phenoVein. We demonstrate the power of phenoVein on a set of previously described vein structure mutants of Arabidopsis (hemivenata, ondulata3, andasymmetric leaves2-101) compared with wild-type accessions Columbia-0 and Landsbergerecta-0. phenoVein is freely available as open-source software.
• Background and Aims The capacity for fast-growth recovery after de-submergence is important for establishment of riparian species in a water-level-fluctuation zone. Recovery patterns of two wetland ...plants, Alternanthera philoxeroides and Hemarthria altissima, showing 'escape' and 'quiescence' responses, respectively, during submergence were investigated. • Methods Leaf and root growth and photosynthesis were monitored continuously during 10 d of recovery following 20 d of complete submergence. Above-and below-ground dry weights, as well as carbohydrate concentrations, were measured several times during the experiment. • Key Results Both species remobilized stored carbohydrate during submergence. Although enhanced internode elongation depleted the carbohydrate storage in A. philoxeroides during submergence, this species resumed leaf growth 3 d after de-submergence concomitant with restoration of the maximal photosynthetic capacity. In contrast, some sucrose was conserved in shoots of H. altissima during submergence, which promoted rapid re-growth of leaves 2 d after de-submergence and earlier than the full recovery of photosynthesis. The recovery of root growth was delayed by 1-2 d compared with leaves in both species. • Conclusions Submergence tolerance of the escape and quiescence strategies entails not only the corresponding regulation of growth, carbohydrate catabolism and energy metabolism during submergence but also co-ordinated recovery of photosynthesis, growth and carbohydrate partitioning following de-submergence.
In this paper we extend anisotropic diffusion with a diffusion tensor to be applicable to data that is well modeled by linear models. We focus on its variational theory, and investigate simple ...discretizations and their performance on synthetic data fulfilling the underlying linear models. To this end, we first show that standard anisotropic diffusion with a diffusion tensor is directly linked to a data model describing single orientations. In the case of spatio-temporal data this model is the well known brightness constancy constraint equation often used to estimate optical flow. Using this observation, we construct extended anisotropic diffusion schemes that are based on more general linear models. These schemes can be thought of as higher order anisotropic diffusion. As an example we construct schemes for noise reduction in the case of two orientations in 2d images. By comparison to the denoising result via standard single orientation anisotropic diffusion, we demonstrate the better suited behavior of the novel schemes for double orientation data.
We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then ...post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.
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•We introduce a novel method for finding and tracking multiple plant leaves.•We can automatically measure relevant plant parameters (e.g. leaf growth rates).•The procedure has three ...stages: preprocessing, leaf segmentation, and tracking.•The method was tested on infrared tobacco-plant image sequences.•The framework was used in a EU project Garnics as a robotic perception unit.
In this paper, we present a novel multi-level procedure for finding and tracking leaves of a rosette plant, in our case up to 3 weeks old tobacco plants, during early growth from infrared-image sequences. This allows measuring important plant parameters, e.g. leaf growth rates, in an automatic and non-invasive manner. The procedure consists of three main stages: preprocessing, leaf segmentation, and leaf tracking. Leaf-shape models are applied to improve leaf segmentation, and further used for measuring leaf sizes and handling occlusions. Leaves typically grow radially away from the stem, a property that is exploited in our method, reducing the dimensionality of the tracking task. We successfully tested the method on infrared image sequences showing the growth of tobacco-plant seedlings up to an age of about 30days, which allows measuring relevant plant growth parameters such as leaf growth rate. By robustly fitting a suitably modified autocatalytic growth model to all growth curves from plants under the same treatment, average plant growth models could be derived. Future applications of the method include plant-growth monitoring for optimizing plant production in green houses or plant phenotyping for plant research.
Stress caused by environmental factors evokes dynamic changes in plant phenotypes. In this study, we deciphered simultaneously the reaction of plant growth and chlorophyll fluorescence related ...parameters using a novel approach which combines existing imaging technologies (GROWSCREEN FLUORO). Three different abiotic stress situations were investigated demonstrating the benefit of this approach to distinguish between effects related to (1) growth, (2) chlorophyll-fluorescence, or (3) both of these aspects of the phenotype. In a drought stress experiment with more than 500 plants, poly(ADP-ribose) polymerase (PARP) deficient lines of Arabidopsis thaliana (L.) Heynh showed increased relative growth rates (RGR) compared with C24 wild-type plants. In chilling stress, growth of PARP and C24 lines decreased rapidly, followed by a decrease in Fv/Fm. Here, PARP-plants showed a more pronounced decrease of Fv/Fm than C24, which can be interpreted as a more efficient strategy for survival in mild chilling stress. Finally, the reaction of Nicotiana tabacum L. to altered spectral composition of the intercepted light was monitored as an example of a moderate stress situation that affects chlorophyll-fluorescence related, but not growth-related parameters. The examples investigated in this study show the capacity for improved plant phenotyping based on an automated and simultaneous evaluation of growth and photosynthesis at high throughput.
Automated microscopy techniques combined with high-throughput microfluidic cultivation systems provide unique insights into living microorganisms, enabling hundreds of experiments to be performed in ...parallel. Such setups generate large data sets with rich cellular features that need to be extracted to arrive at quantitative insights. The sheer amount of recorded time-lapse images requires reliable automated processing. While recent advances in deep learning methods have enabled automated processing, these methods rely on large-scale and precisely annotated data sets, often not available for new organisms or custom imaging modalities.
To overcome the annotated data bottleneck, particularly in the microbial domain, we present the open-source ObiWan-Microbi platform providing a fast workflow for large-scale annotation of up to hundred thousands of segmentation and tracking annotations in time-lapse imaging data. ObiWan-Microbi focuses on easy-to-use semi-automated annotation in the browser eliminating the need for local installation or accelerator hardware, encourages FAIR data management using OMERO, and provides convenient collaborative cloud deployment to simplify the creation and long-term development of large-scale annotated data sets. The public availability of such benchmark data sets has the potential to improve data-driven methods, increase comparability among them, and is an essential step towards reliable automated image processing in microbial live-cell microscopy.
BACKGROUND: Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral ...absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device. RESULTS: The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content. CONCLUSIONS: The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.
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