ArtSeg-Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations.
Ontology highlight
ABSTRACT:
SUBMITTER: Ali MAS
PROVIDER: S-EPMC9259686 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While deep learning models have given encouraging results on this problem, the most powerful approaches have not yet been tested for attacking it. Here, we review and evaluate state-of-the-art very deep convolutional neural network architectures and training strategies for segmenting nuclei from brightfield cell images. We tested U-Net as a baseline model; considered U-Net++, Tiramisu, and DeepLabv3+ as latest instances of advanced families of segmentation models; and propose PPU-Net, a novel light-weight alternative. The deeper architectures outperformed standard U-Net and results from previous studies on the challenging brightfield images, with balanced pixel-wise accuracies of up to 86%. PPU-Net achieved this performance with 20-fold fewer parameters than the comparably accurate methods. All models perform better on larger nuclei and in sparser images. We further confirmed that in the absence of plentiful training data, augmentation and pretraining on other data improve performance. In particular, using only 16 images with data augmentation is enough to achieve a pixel-wise F1 score that is within 5% of the one achieved with a full data set for all models. The remaining segmentation errors are mainly due to missed nuclei in dense regions, overlapping cells, and imaging artifacts, indicating the major outstanding challenges.
| S-EPMC8458686 | biostudies-literature
Project description:MotivationSingle-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process.ResultsWe developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism.Availability and implementationThe synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8825468 | biostudies-literature
Project description:BackgroundNucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.ResultsWe analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance.ConclusionsWe conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.
| S-EPMC6744696 | biostudies-literature
Project description:Intravital fluorescence microscopy, through extended penetration depth and imaging resolution, provides the ability to image at cellular and subcellular resolution in live animals, presenting an opportunity for new insights into in vivo biology. Unfortunately, physiological induced motion components due to respiration and cardiac activity are major sources of image artifacts and impose severe limitations on the effective imaging resolution that can be ultimately achieved in vivo. Here we present a novel imaging methodology capable of automatically removing motion artifacts during intravital microscopy imaging of organs and orthotopic tumors. The method is universally applicable to different laser scanning modalities including confocal and multiphoton microscopy, and offers artifact free reconstructions independent of the physiological motion source and imaged organ. The methodology, which is based on raw data acquisition followed by image processing, is here demonstrated for both cardiac and respiratory motion compensation in mice heart, kidney, liver, pancreas and dorsal window chamber.
| S-EPMC3968488 | biostudies-other
Project description:BackgroundRecent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data.ResultsWe present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities.ConclusionsSegmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .
| S-EPMC8141214 | biostudies-literature
Project description:Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.
| S-EPMC7275059 | biostudies-literature
Project description:SCOPe (Structural Classification of Proteins-extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high-quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 Protein Data Bank entries, double the 38,221 structures classified in SCOP.
| S-EPMC5272801 | biostudies-literature
Project description:Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.
| S-EPMC6233481 | biostudies-other
Project description:Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use their clinical notes to speculate about disease existence (differential diagnosis) or to state that the disease has been ruled out. To train classifiers for disambiguating disease references, previous researchers built training sets by manually annotating sentences. We show how to create very large training sets without the need for manual annotation. We obtain state-of- the-art classification performance with a bidirectional long short-term memory model trained to distinguish disease references between patients with or without the disease diagnosis in veterinary clinical notes.
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| S-EPMC10951928 | biostudies-literature