| 1. |
- Gupta, Ankit
(författare)
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Adapting Deep Learning for Microscopy: Interaction, Application, and Validation
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. Microscopy images contain rich but sparse information, as typically, only small regions in the images are relevant for further study. Image analysis is a crucial tool for biologists in the objective interpretation and extraction of quantitative measurements from microscopy data. Recently, deep learning techniques have shown superior performance in various image analysis tasks. The models learn feature representations from the data by optimizing for a task. However, the techniques require a significant amount of annotated data to perform well. Domain experts are required to annotate microscopy data, making it expensive and time-consuming. The models offer no insight into their prediction, and the learned features are not directly interpretable. This poses challenges to the reliable utilization of the technique in high-trust applications such as drug discovery or disease detection. High data variability in microscopy and poor generalization performance of deep learning models further increase the difficulty in general usage of the technique. The work in this thesis presents frameworks and methods to solve the practical challenges of applying deep learning in microscopy. The application-specific evaluation approaches were presented to validate the approaches, aiming to increase trust in the system. The major contributions of this work are as follows. Papers I and III present human-in-the-loop frameworks for quick adaption of deep learning to new data and for improving models' performance based on human input in visual explanations provided by the model, respectively. Paper II proposes a template-matching approach to improve user interactions in the framework proposed in Paper I. Papers III and IV present architectural modifications in the deep learning models proposed for better visual explanation and image-to-image translation, respectively. Papers IV and V present biologically relevant evaluations of approaches, i.e., analysis of the deep learning models in relation to the biological task.This thesis is aimed towards better utilization and adaptation of the DL methods and techniques to the microscopy data. We show that the annotation burden for the user can be significantly reduced by intuitive annotation frameworks and using contemporary deep-learning paradigms. We further propose architectural modifications in the models to adapt to the requirements and demonstrate the utility of application-specific analysis in microscopy.
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| 2. |
- Harrison, Philip John, 1977-
(författare)
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Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Image cytometry is the analysis of cell properties from microscopy image data and is used ubiquitously in basic cell biology, medical diagnosis and drug development. In recent years deep learning has shown impressive results for many image cytometry tasks, including image processing, segmentation, classification and detection. Deep learning enables a more data-driven and end-to-end approach than was previously possible with conventional methods. This thesis investigates deep learning-based approaches for assessing cellular morphological responses to drug perturbations. In paper I we demonstrated the benefit of combining convolutional neural networks and transfer learning for predicting mechanism of action and nucleus translocation. In paper II we showed, using convolutional and recurrent neural networks applied to time-lapse microscopy data, that it is possible to predict if mRNA delivery via nanoparticles has been effective based on cell morphology changes at time points prior to the protein production evidence of successful delivery. In paper III we used convolutional neural networks, adversarial training and privileged information to faithfully generate fluorescence imaging channels of adipocyte cells from their corresponding z-stack of brightfield images. Our models were both faithful at the fluorescence image level and at the level of the features extracted from these images, features that are commonly used for downstream analysis, including the design of effective drug therapies. In paper IV we showed that convolutional neural networks trained on brightfield image data provide similar, and in some cases superior, performance to models trained on fluorescence image data for predicting mechanism of action, due to the brightfield images possessing additional information not available in the fluorescence images. In paper V we applied deep learning models to brightfield time-lapse image data to explore the evolution of cellular morphological changes after drug administration for a diverse set of compounds, compounds that are often used as positive controls in image-based assays.
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| 3. |
- Harrison, Philip John, et al.
(författare)
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Evaluating the utility of brightfield image data for mechanism of action prediction
- 2023
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:7
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Tidskriftsartikel (refereegranskat)abstract
- Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
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| 4. |
- Pielawski, Nicolas
(författare)
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Learning-based prediction, representation, and multimodal registration for bioimage processing
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Microscopy and imaging are essential to understanding and exploring biology. Modern staining and imaging techniques generate large amounts of data resulting in the need for automated analysis approaches. Many earlier approaches relied on handcrafted feature extractors, while today's deep-learning-based methods open up new ways to analyze data automatically.Deep learning has become popular in bioimage processing as it can extract high-level features describing image content (Paper III). The work in this thesis explores various aspects and limitations of machine learning and deep learning with applications in biology. Learning-based methods have generalization issues on out-of-distribution data points, and methods such as uncertainty estimation (Paper II) and visual quality control (Paper V) can provide ways to mitigate those issues. Furthermore, deep learning methods often require large amounts of data during training. Here the focus is on optimizing deep learning methods to meet current computational capabilities and handle the increasing volume and size of data (Paper I). Model uncertainty and data augmentation techniques are also explored (Papers II and III).This thesis is split into chapters describing the main components of cell biology, microscopy imaging, and the mathematical and machine-learning theories to give readers an introduction to biomedical image processing. The main contributions of this thesis are deep-learning methods for reconstructing patch-based segmentation (Paper I) and pixel regression of traction force images (Paper II), followed by methods for aligning images from different sensors in a common coordinate system (named multimodal image registration) using representation learning (Paper III) and Bayesian optimization (Paper IV). Finally, the thesis introduces TissUUmaps 3, a tool for visualizing multiplexed spatial transcriptomics data (Paper V). These contributions provide methods and tools detailing how to apply mathematical frameworks and machine-learning theory to biology, giving us concrete tools to improve our understanding of complex biological processes.
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| 5. |
- Andersson, Axel, et al.
(författare)
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Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning
- 2023
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Ingår i: Graph-Based Representations in Pattern Recognition. - Cham : Springer. - 9783031427947 - 9783031427954 ; , s. 139-148
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Konferensbidrag (refereegranskat)abstract
- The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.
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| 6. |
- Hallström, Erik, et al.
(författare)
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Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy
- 2023
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:11
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Tidskriftsartikel (refereegranskat)abstract
- Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.
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| 7. |
- Pielawski, Nicolas, et al.
(författare)
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TissUUmaps 3 : Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data
- 2023
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Background and objectives: Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples.Methods: Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.Results: We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods.Conclusion: TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.
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| 8. |
- Sountoulidis, Alexandros, et al.
(författare)
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A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung
- 2023
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Ingår i: Nature Cell Biology. - : Springer Nature. - 1465-7392 .- 1476-4679.
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Tidskriftsartikel (refereegranskat)abstract
- Sountoulidis et al. provide a spatial gene expression atlas of human embryonic lung during the first trimester of gestation and identify 83 cell identities corresponding to stable cell types or transitional states. The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.
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