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Automatic facial beauty analysis has become an emerging research topic. Despite some achieved advances, current methods and systems suffer from at least two limitations. Firstly, many developed systems rely on the use of ad-hoc hand-crafted features that were designed for generic pattern recognition problems. Secondly, while Deep Convolutional Neural Nets (DCNN) have been recently demonstrated to be a promising area of research in statistical machine learning, their use for automatic face beauty analysis may not guarantee optimal performances due to the use of a limited amount of face images with beauty scores. In this paper, we attempt to overcome these two main limitations by jointly exploiting two tricks. First, instead of using hand-crafted face features we use deep features of a pre-trained DCNN able to generate a high-level representation of a face image. Second, we exploit manifold learning theory and deploy three graph-based semi-supervised learning methods in order to enrich model learning without the need of additional labeled face images. These schemes perform graph-based score propagation. The proposed schemes were tested on three public datasets for beauty analysis: SCUT-FBP, M2B, and SCUT-FBP5500. These experiments, as well as many comparisons with supervised schemes, show that the scheme coined Kernel Flexible Manifold Embedding compares favorably with many supervised schemes. They also show that its performances in terms of error prediction and Pearson Correlation are better than those reported for the used datasets. [ABSTRACT FROM AUTHOR]

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain cancer-related mortality has risen from 140,000 to 250,000, accompanied by a doubling in new diagnoses from 175,000 to 350,000. In response, magnetic resonance imaging (MRI) has emerged as a pivotal diagnostic tool, facilitating early detection and treatment planning. However, the translation of deep learning approaches to brain cancer diagnosis faces a critical obstacle: the scarcity of public clinical datasets reflecting real-world complexities. This study aims to bridge this gap through a comprehensive exploration and augmentation of training data. Initially, a battery of pre-trained deep models undergoes evaluation on a main brain cancer MRI "BT-MRI" dataset, yielding remarkable performance metrics, including 100% accuracy, precision, recall, and F1-Score, substantiated by the Score-CAM methodology. This initial success underscores the potential of deep learning in brain cancer diagnosis. Subsequently, the model's efficacy undergoes further scrutiny using a supplementary brain cancer MRI "BCD-MRI" dataset, affirming its robustness and applicability across diverse datasets. However, the ultimate litmus test lies in confronting the model with synthetic testing datasets crafted to emulate real-world scenarios. The synthetic testing datasets, a BCD-MRI testing sub-dataset enriched with noise, blur, and simulated patient motion, reveal a sobering reality: the model's performance plummets, exposing inherent limitations in generalization. To address this issue, a diverse set of optimization strategies and augmentation techniques, ranging from diverse optimizers to sophisticated data augmentation methods, are exhaustively explored. Despite these efforts, the problem of generalization persists. The breakthrough emerges with the integration of noise and blur as augmentation techniques during the training process. Leveraging Gaussian noise and Gaussian blur kernels, the model undergoes a transformative evolution, exhibiting newfound robustness and resilience. Retesting the refined model against the challenging synthetic datasets reveals a remarkable transformation, with performance metrics witnessing a notable ascent. This achievement underscores the important role of correct selection of data augmentation in fortifying the generalization of deep learning models for brain cancer diagnosis. This study not only advances the frontiers of diagnostic precision in brain cancer but also underscores the paramount importance of methodological rigor and innovation in confronting the complexities of real-world clinical scenarios., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named NucExM, are publicly available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022. Published by Elsevier Inc.)

Background and Objective: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species., Methods: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation., Results: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets., Conclusions: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications make neither the code nor the full training details public, leading to reproducibility issues and dubious model comparisons. Thus, following a recent code of best practices in the field, we present an extensive study of the state-of-the-art architectures and compare them to different variations of U-Net-like models for this task. To unveil the impact of architectural novelties, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters has been performed, running each configuration multiple times to measure their stability. Using this methodology, we found very stable architectures and training configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset and outperform all previous works on two other available datasets: Lucchi++ and Kasthuri++. The code and its documentation are publicly available at https://github.com/danifranco/EM_Image_Segmentation ., (© 2021. The Author(s).)

While numerous transgenic mouse strains have been produced to model the formation of amyloid-β (Aβ) plaques in the brain, efficient methods for whole-brain 3D analysis of Aβ deposits have to be validated and standardized. Moreover, routine immunohistochemistry performed on brain slices precludes any shape analysis of Aβ plaques, or require complex procedures for serial acquisition and reconstruction. The present study shows how in-line (propagation-based) X-ray phase-contrast tomography (XPCT) combined with ethanol-induced brain sample dehydration enables hippocampus-wide detection and morphometric analysis of Aβ plaques. Performed in three distinct Alzheimer mouse strains, the proposed workflow identified differences in signal intensity and 3D shape parameters: 3xTg displayed a different type of Aβ plaques, with a larger volume and area, greater elongation, flatness and mean breadth, and more intense average signal than J20 and APP/PS1. As a label-free non-destructive technique, XPCT can be combined with standard immunohistochemistry. XPCT virtual histology could thus become instrumental in quantifying the 3D spreading and the morphological impact of seeding when studying prion-like properties of Aβ aggregates in animal models of Alzheimer's disease. This is Part II of a series of two articles reporting the value of in-line XPCT for virtual histology of the brain; Part I shows how in-line XPCT enables 3D myelin mapping in the whole rodent brain and in human autopsy brain tissue., Competing Interests: The authors declare no conflicts of interest., (© 2022 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.

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.

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level accuracy on these small datasets are robust in practice. To this end, we introduce the MitoEM dataset, a 3D mitochondria instance segmentation dataset with two (30 μ m) 3 volumes from human and rat cortices respectively, 3, 600× larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in terms of shape and density. For evaluation, we tailor the implementation of the average precision (AP) metric for 3D data with a 45× speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data: https://donglaiw.github.io/page/mitoEM/index.html.

Objectives: We provide the description and comparative analysis of all the human fossil remains found at Axlor during the excavations carried out by J. M. de Barandiarán from 1967 to 1974: a cranial vault fragment and seven teeth, five of which likely belonged to the same individual, although two are currently lost. Our goal is to describe in detail all these human remains and discuss both their taxonomic attribution and their stratigraphic context., Materials and Methods: We describe external and internal anatomy, and use classic and geometric morphometrics. The teeth from Axlor are compared to Neandertals, Upper Paleolithic, and recent modern humans., Results: Two teeth (a left dm 2 , a left di 1 ) and the parietal fragment show morphological features consistent with a Neandertal classification, and were found in an undisturbed Mousterian context. The remaining three teeth (plus the two lost ones), initially classified as Neandertals, show morphological features and a general size that are more compatible with their classification as modern humans., Discussion: A left parietal fragment (Level VIII) from a single probably adult Neandertal individual was recovered during the old excavations performed by Barandiarán. Additionally, two different Neandertal children lost deciduous teeth during the formations of levels V (left di 1 ) and IV (right dm 2 ). In addition, a modern human individual is represented by five remains (two currently lost) from a complex stratigraphic setting. Some of the morphological features of these remains suggest that they may represent one of the scarce examples of Upper Paleolithic modern human remains in the northern Iberian Peninsula, which should be confirmed by direct dating., (© 2019 Wiley Periodicals, Inc.)

Information within the brain travels from neuron to neuron across billions of synapses. At any given moment, only a small subset of neurons and synapses are active, but finding the active synapses in brain tissue has been a technical challenge. Here we introduce SynTagMA to tag active synapses in a user-defined time window. Upon 395-405 nm illumination, this genetically encoded marker of activity converts from green to red fluorescence if, and only if, it is bound to calcium. Targeted to presynaptic terminals, preSynTagMA allows discrimination between active and silent axons. Targeted to excitatory postsynapses, postSynTagMA creates a snapshot of synapses active just before photoconversion. To analyze large datasets, we show how to identify and track the fluorescence of thousands of individual synapses in an automated fashion. Together, these tools provide an efficient method for repeatedly mapping active neurons and synapses in cell culture, slice preparations, and in vivo during behavior.

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.

Strongly polyphenic social insects provide excellent models to examine the neurobiological basis of division of labor. Turtle ants, Cephalotes varians, have distinct minor worker, soldier, and reproductive (gyne/queen) morphologies associated with their behavioral profiles: small-bodied task-generalist minors lack the phragmotic shield-shaped heads of soldiers, which are specialized to block and guard the nest entrance. Gynes found new colonies and during early stages of colony growth overlap behaviorally with soldiers. Here we describe patterns of brain structure and synaptic organization associated with division of labor in C. varians minor workers, soldiers, and gynes. We quantified brain volumes, determined scaling relationships among brain regions, and quantified the density and size of microglomeruli, synaptic complexes in the mushroom body calyxes important to higher-order processing abilities that may underpin behavioral performance. We found that brain volume was significantly larger in gynes; minor workers and soldiers had similar brain sizes. Consistent with their larger behavioral repertoire, minors had disproportionately larger mushroom bodies than soldiers and gynes. Soldiers and gynes had larger optic lobes, which may be important for flight and navigation in gynes, but serve different functions in soldiers. Microglomeruli were larger and less dense in minor workers; soldiers and gynes did not differ. Correspondence in brain structure despite differences in soldiers and gyne behavior may reflect developmental integration, suggesting that neurobiological metrics not only advance our understanding of brain evolution in social insects, but may also help resolve questions of the origin of novel castes., Competing Interests: The authors have declared that no competing interests exist.

The human deubiquitinases USP12 and USP46 are very closely related paralogs with critical functions as tumor suppressors. The catalytic activity of these enzymes is regulated by two cofactors: UAF1 and WDR20. USP12 and USP46 show nearly 90% amino acid sequence identity and share some cellular activities, but have also evolved non-overlapping functions. We hypothesized that, correlating with their functional divergence, the subcellular localization of USP12 and USP46 might be differentially regulated by their cofactors. We used confocal and live microscopy analyses of epitope-tagged proteins to determine the effect of UAF1 and WDR20 on the localization of USP12 and USP46. We found that WDR20 differently modulated the localization of the DUBs, promoting recruitment of USP12, but not USP46, to the plasma membrane. Using site-directed mutagenesis, we generated a large set of USP12 and WDR20 mutants to characterize in detail the mechanisms and sequence determinants that modulate the subcellular localization of the USP12/UAF1/WDR20 complex. Our data suggest that the USP12/UAF1/WDR20 complex dynamically shuttles between the plasma membrane, cytoplasm and nucleus. This shuttling involved active nuclear export mediated by the CRM1 pathway, and required a short N-terminal motif ( 1 MEIL 4 ) in USP12, as well as a novel nuclear export sequence ( 450 MDGAIASGVSKFATLSLHD 468 ) in WDR20. In conclusion, USP12 and USP46 have evolved divergently in terms of cofactor binding-regulated subcellular localization. WDR20 plays a crucial role in as a "targeting subunit" that modulates CRM1-dependent shuttling of the USP12/UAF1/WDR20 complex between the plasma membrane, cytoplasm and nucleus., (Copyright © 2018 Elsevier GmbH. All rights reserved.)

Bone histomorphometry allows quantitative evaluation of bone micro-architecture, bone formation, and bone remodeling by providing an insight to cellular changes. Histomorphometry plays an important role in monitoring changes in bone properties because of systemic skeletal diseases like osteoporosis and osteomalacia. Besides, quantitative evaluation plays an important role in fracture healing studies to explore the effect of biomaterial or drug treatment. However, until today, to our knowledge, bone histomorphometry remain time-consuming and expensive. This incited us to set up an open-source freely available semi-automated solution to measure parameters like trabecular area, osteoid area, trabecular thickness, and osteoclast activity. Here in this study, the authors present the adaptation of Trainable Weka Segmentation plugin of ImageJ to allow fast evaluation of bone parameters (trabecular area, osteoid area) to diagnose bone related diseases. Also, ImageJ toolbox and plugins (BoneJ) were adapted to measure osteoclast activity, trabecular thickness, and trabecular separation. The optimized two different scripts are based on ImageJ, by providing simple user-interface and easy accessibility for biologists and clinicians. The scripts developed for bone histomorphometry can be optimized globally for other histological samples. The showed scripts will benefit the scientific community in histological evaluation.

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.

Summary: 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., Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation ., Contact: ignacio.arganda@ehu.eus., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

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.

Motivation: 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., Results: 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., Availability and Implementation: MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: david.legland@nantes.inra.frSupplementary information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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.

Unlabelled: NucleusJ is a simple and user-friendly ImageJ plugin dedicated to the characterization of nuclear morphology and chromatin organization in 3D. Starting from image stacks, the nuclear boundary is delimited by combining the Otsu segmentation method with optimization of nuclear sphericity. Chromatin domains are segmented by partitioning the nucleus using a 3D watershed algorithm and by thresholding a contrast measure over the resulting regions. As output, NucleusJ quantifies 15 parameters including shape and size of nuclei as well as intra-nuclear objects and their position within the nucleus. A step-by-step documentation is available for self-training, together with data sets of nuclei with different nuclear organization., Availability and Implementation: Dataset of nuclei is available at https://www.gred-clermont.fr/media/WorkDirectory.zip. NucleusJ is available at http://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:nuclear_analysis_plugin:start., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.)

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here, we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate-early-gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP+ neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Chemotopic odour representations in the olfactory bulb are transferred to multiple forebrain areas and translated into appropriate output responses. However, a comprehensive projection map of bulbar output neurons at single-axon resolution is lacking in vertebrates. Here we unravel a projectome of the zebrafish olfactory bulb through genetic single-neuron tracing and image registration. We show that five major target regions receive distinct modes of projections from olfactory bulb glomeruli. The central portion of posterior telencephalon receives non-selective, interspersed inputs from all glomeruli, whereas the ventral telencephalon is diffusely innervated by axons from particular glomerular clusters. The right habenula and posterior tuberculum (diencephalic nuclei) receive convergent inputs from restricted and all glomerular clusters, respectively. The bulbar recurrent projections are coarsely topographic. Thus, the primary chemotopic organization is transformed into distinct sensory representations in higher olfactory centres. These findings provide a framework to understand general principles as well as species-specific features in decoding of odour information.

Background: Studying how individual cells spatially and temporally organize within the embryo is a fundamental issue in modern developmental biology to better understand the first stages of embryogenesis. In order to perform high-throughput analyses in three-dimensional microscopic images, it is essential to be able to automatically segment, classify and track cell nuclei. Many 3D/4D segmentation and tracking algorithms have been reported in the literature. Most of them are specific to particular models or acquisition systems and often require the fine tuning of parameters., Results: We present a new automatic algorithm to segment and simultaneously classify cell nuclei in 3D/4D images. Segmentation relies on training samples that are interactively provided by the user and on an iterative thresholding process. This algorithm can correctly segment nuclei even when they are touching, and remains effective under temporal and spatial intensity variations. The segmentation is coupled to a classification of nuclei according to cell cycle phases, allowing biologists to quantify the effect of genetic perturbations and drug treatments. Robust 3D geometrical shape descriptors are used as training features for classification. Segmentation and classification results of three complete datasets are presented. In our working dataset of the Caenorhabditis elegans embryo, only 21 nuclei out of 3,585 were not detected, the overall F-score for segmentation reached 0.99, and more than 95% of the nuclei were classified in the correct cell cycle phase. No merging of nuclei was found., Conclusion: We developed a novel generic algorithm for segmentation and classification in 3D images. The method, referred to as Adaptive Generic Iterative Thresholding Algorithm (AGITA), is freely available as an ImageJ plug-in.

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.