| 1. |
- Kårsnäs, Andreas, 1977-
(författare)
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Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation. In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.
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| 2. |
- Allalou, Amin, 1981-
(författare)
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Methods for 2D and 3D Quantitative Microscopy of Biological Samples
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening. It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions. HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.
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| 3. |
- Gavrilovic, Milan, 1981-
(författare)
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Spectral Image Processing with Applications in Biotechnology and Pathology
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans.Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias.This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations.The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.
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| 4. |
- Gedda, Magnus, 1978-
(författare)
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Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques : With Focus on Images from Cryo-Electron Tomography
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the emergence of new imaging techniques, researchers are always eager to push the boundaries by examining objects either smaller or further away than what was previously possible. The development of image analysis techniques has greatly helped to introduce objectivity and coherence in measurements and decision making. It has become an essential tool for facilitating both large-scale quantitative studies and qualitative research. In this Thesis, methods were developed for analysis of low-resolution (in respect to the size of the imaged objects) three-dimensional (3D) images with low signal-to-noise ratios (SNR) applied to images from cryo-electron tomography (cryo-ET) and fluorescence microscopy (FM). The main focus is on methods of low complexity, that take into account both grey-level and shape information, to facilitate large-scale studies. Methods were developed to localise and represent complex macromolecules in images from cryo-ET. The methods were applied to Immunoglobulin G (IgG) antibodies and MET proteins. The low resolution and low SNR required that grey-level information was utilised to create fuzzy representations of the macromolecules. To extract structural properties, a method was developed to use grey-level-based distance measures to facilitate decomposition of the fuzzy representations into sub-domains. The structural properties of the MET protein were analysed by developing a analytical curve representation of its stalk. To facilitate large-scale analysis of structural properties of nerve cells, a method for tracing neurites in FM images using local path-finding was developed. Both theoretical and implementational details of computationally heavy approaches were examined to keep the time complexity low in the developed methods. Grey-weighted distance definitions and various aspects of their implementations were examined in detail to form guidelines on which definition to use in which setting and which implementation is the fastest. Heuristics were developed to speed up computations when calculating grey-weighted distances between two points. The methods were evaluated on both real and synthetic data and the results show that the methods provide a step towards facilitating large-scale studies of images from both cryo-ET and FM.
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| 5. |
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| 6. |
- 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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| 7. |
- Matuszewski, Damian J., 1988-
(författare)
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Image and Data Analysis for Biomedical Quantitative Microscopy
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents automatic image and data analysis methods to facilitate and improve microscopy-based research and diagnosis. New technologies and computational tools are necessary for handling the ever-growing amounts of data produced in life science. The thesis presents methods developed in three projects with different biomedical applications.In the first project, we analyzed a large high-content screen aimed at enabling personalized medicine for glioblastoma patients. We focused on capturing drug-induced cell-cycle disruption in fluorescence microscopy images of cancer cell cultures. Our main objectives were to identify drugs affecting the cell-cycle and to increase the understanding of different drugs’ mechanisms of action. Here we present tools for automatic cell-cycle analysis and identification of drugs of interest and their effective doses.In the second project, we developed a feature descriptor for image matching. Image matching is a central pre-processing step in many applications. For example, when two or more images must be matched and registered to create a larger field of view or to analyze differences and changes over time. Our descriptor is rotation-, scale-, and illumination-invariant and it has a short feature vector which makes it computationally attractive. The flexibility to combine it with any feature detector and the customization possibility make it a very versatile tool.In the third project, we addressed two general problems for bridging the gap between deep learning method development and their use in practical scenarios. We developed a method for convolutional neural network training using minimally annotated images. In many biomedical applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, ambiguous morphology, image quality, or the expert knowledge and time it requires. The minimal annotations, in this case, consist of center-points or centerlines of target objects of approximately known size. We demonstrated our training method in a challenging application of a multi-class semantic segmentation of viruses in transmission electron microscopy images. We also systematically explored the influence of network architecture hyper-parameters on its size and performance and show the possibility to substantially reduce the size of a network without compromising its performance.All methods in this thesis were designed to work with little or no input from biomedical experts but of course, require fine-tuning for new applications. The usefulness of the tools has been demonstrated by collaborators and other researchers and has inspired further development of related algorithms.
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| 8. |
- Partel, Gabriele, 1988-
(författare)
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Image and Data Analysis for Spatially Resolved Transcriptomics : Decrypting fine-scale spatial heterogeneity of tissue's molecular architecture
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Our understanding of the biological complexity in multicellular organisms has progressed at tremendous pace in the last century and even more in the last decades with the advent of sequencing technologies that make it possible to interrogate the genome and transcriptome of individual cells. It is now possible to even spatially profile the transcriptomic landscape of tissue architectures to study the molecular organization of tissue heterogeneity at subcellular resolution. Newly developed spatially resolved transcriptomic techniques are producing large amounts of high-dimensional image data with increasing throughput, that need to be processed and analysed for extracting biological relevant information that has the potential to lead to new knowledge and discoveries. The work included in this thesis aims to provide image and data analysis tools for serving this new developing field of spatially resolved transcriptomics to fulfill its purpose. First, an image analysis workflow is presented for processing and analysing images acquired with in situ sequencing protocols, aiming to extract and decode molecular features that map the spatial transcriptomic landscape in tissue sections. This thesis also presents computational methods to explore and analyse the decoded spatial gene expression for studying the spatial molecular heterogeneity of tissue architectures at different scales. In one case, it is demonstrated how dimensionality reduction and clustering of the decoded gene expression spatial profiles can be exploited and used to identify reproducible spatial compartments corresponding to know anatomical regions across mouse brain sections from different individuals. And lastly, this thesis presents an unsupervised computational method that leverages advanced deep learning techniques on graphs to model the spatial gene expression at cellular and subcellular resolution. It provides a low dimensional representation of spatial organization and interaction, finding functional units that in many cases correspond to different cell types in the local tissue environment, without the need for cell segmentation.
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| 9. |
- Stadler, Charlotte, 1982-
(författare)
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Towards subcellular localization of the human proteome using bioimaging
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the publication of the complete sequence of the human genome in 2003 there has been great interest in exploring the functions of the proteins encoded by the genes. To reveal the function of each and every protein, investigation of protein localization at the subcellular level has become a central focus in this research area, since the localization and function of a protein is closely related. The objective of the studies presented in this doctoral thesis was to systematically explore the human proteome at the subcellular level using bioimaging and to develop techniques for validation of the results obtained.A common imaging technique for protein detection is immunofluorescence (IF), where antibodies are used to target proteins in fixated cells. A fixation protocol suitable for large-scale IF studies was developed and optimized to work for a broad set of proteins. As the technique relies on antibodies, validation of their specificity to the target protein is crucial. A platform based on siRNA gene silencing in combination with IF was set-up to evaluate antibody specificity by quantitative image analysis before and after suppression of its target protein. As a proof of concept, the platform was then used for validation of 75 antibodies, proving it to be applicable for validation of antibodies in a systematic manner.Because of the fixation, there is a common concern about how well IF data reflects the in vivo subcellular distribution of proteins. To address this, 500 proteins were tagged with green fluorescent protein (GFP) and used to compare protein localization results between IF to those achieved using GFP tagged proteins in live cells. It was concluded that protein localization data from fixated cells satisfactory represented the situation in vivo and together exhibit a powerful approach for confirming localizations of yet uncharacterized proteins.Finally, a global analysis based on IF data of approximately 20 % of the human proteome was performed, providing a first overview of the subcellular landscape in three different cell lines. It was found that the intracellular distribution of proteins is complex, with many proteins occurring in several organelles. The results also confirmed the close relationship between protein function and localization, which in a way further strengthens the accuracy of the IF approach for detection of proteins at the subcellular level.
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