| 1. |
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| 2. |
- Schmidt, Linnéa, et al.
(författare)
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Case-specific potentiation of glioblastoma drugs by pterostilbene
- 2016
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Ingår i: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 7:45, s. 73200-73215
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Tidskriftsartikel (refereegranskat)abstract
- Glioblastoma multiforme (GBM, astrocytoma grade IV) is the most common malignant primary brain tumor in adults. Addressing the shortage of effective treatment options for this cancer, we explored repurposing of existing drugs into combinations with potent activity against GBM cells. We report that the phytoalexin pterostilbene is a potentiator of two drugs with previously reported anti-GBM activity, the EGFR inhibitor gefitinib and the antidepressant sertraline. Combinations of either of these two compounds with pterostilbene suppress cell growth, viability, sphere formation and inhibit migration in tumor GBM cell (GC) cultures. The potentiating effect of pterostilbene was observed to a varying degree across a panel of 41 patient-derived GCs, and correlated in a case specific manner with the presence of missense mutation of EGFR and PIK3CA and a focal deletion of the chromosomal region 1p32. We identify pterostilbene-induced cell cycle arrest, synergistic inhibition of MAPK activity and induction of Thioredoxin interacting protein (TXNIP) as possible mechanisms behind pterostilbene's effect. Our results highlight a nontoxic stilbenoid compound as a modulator of anticancer drug response, and indicate that pterostilbene might be used to modulate two anticancer compounds in well-defined sets of GBM patients.
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| 3. |
- Sintorn, Ida-Maria, et al.
(författare)
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Segmentation of individual pores in 3D paper images
- 2005
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Ingår i: Nordic Pulp & Paper Research Journal. - 0283-2631. ; 20:3, s. 316-319
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we show that the void space in 3D images of paper can be successfully divided into subspaces using digital volume image analysis. The subspaces are denoted pores, which are connected to each other by contractions or narrowings, throats. This division of the void space into individual pores opens the possibility of extracting geometrical features as well as localising where pores with a certain feature are located in the paper. The method is illustrated on a 3D reconstruction from 2D Scanning Electron Microscopy images of a five layered duplex board. Examples of easily extracted features for the segmented pores and plots for some pore features versus the position in the paper are presented.
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| 4. |
- Svensson, Lennart, 1980-, et al.
(författare)
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Rigid template registration in MET images using CUDA
- 2012
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Ingår i: VISAPP 2012. - Rome : SciTePress. - 9789898565037 ; 2, s. 418-422
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Konferensbidrag (refereegranskat)abstract
- Rigid registration is a basic tool in many applications, especially in Molecular Electron Tomography (MET), and also in, e.g., registration of rigid implants in medical images and as initialization for deformable registration. As MET volumes have a low signal to noise ratio, a complete search of the six-dimensional (6D) parameter space is often employed. In this paper, we describe how rigid registration with normalized cross-correlation can be implemented on the GPU using NVIDIA's parallel computing architecture CUDA. We compare the performance to the Colores software and two Matlab implementations, one of which is using the GPU accelerated JACKET library. With well-aligned padding and using CUDA, the performance increases by an order of a magnitude, making it feasible to work with three-dimensional fitness landscapes, here denoted scoring volumes, that are generated on the fly. This will eventually enable the biologists to interactively register macromolecule chains in MET volumes piece by piece.
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| 5. |
- Tammela, Petter, et al.
(författare)
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Asymmetric supercapacitors based on carbon nanofibre and polypyrrole/nanocellulose composite electrodes
- 2015
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Ingår i: RSC Advances. - 2046-2069. ; 5:21, s. 16405-16413
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Tidskriftsartikel (refereegranskat)abstract
- Asymmetric, all-organic supercapacitors (containing an aqueous electrolyte), exhibiting a capacitance of 25 F g-1 (or 2.3 F cm-2) at a current density of 20 mA cm-2 and a maximum cell voltage of 1.6 V, are presented. The devices contain a composite consisting of polypyrrole covered Cladophora cellulose fibres (PPy-cellulose) as the positive electrode while a carbon nanofibre material, obtained by heat treatment of the same PPy-cellulose composite under nitrogen gas flow, serves as the negative electrode. Scanning and transmission electron microscopy combined with X-ray photoelectron spectroscopy data show that the heat treatment gives rise to a porous carbon nanofibre material, topologically almost identical to the original PPy-cellulose composite. The specific gravimetric capacitances of the carbon and the PPy-cellulose electrodes were found to be 59 and 146 F g-1, respectively, while the asymmetric supercapacitors exhibited a gravimetric energy density of 33 J g-1. The latter value is about two times higher than the energy densities obtainable for a symmetric PPy-cellulose device as a result of the larger cell voltage range accessible. The capacitance obtained for the asymmetric devices at a current density of 156 mA cm-2 was 11 F g-1 and cycling stability results further indicate that the capacity loss was about 23% during 1000 cycles employing a current density of 20 mA cm-2. The present results represent a significant step forward towards the realization of all-organic material based supercapacitors with aqueous electrolytes and commercially viable capacitances and energy densities.
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| 6. |
- Adler, Jeremy, et al.
(författare)
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Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous.
- 2019
-
Ingår i: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on deformed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differentiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data.
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| 7. |
- 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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| 8. |
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| 9. |
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| 10. |
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| 11. |
- Bernander, Karl B., et al.
(författare)
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Improving the stochastic watershed
- 2013
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655 .- 1872-7344. ; 34:9, s. 993-1000
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Tidskriftsartikel (refereegranskat)abstract
- The stochastic watershed is an unsupervised segmentation tool recently proposed by Angulo and Jeulin. By repeated application of the seeded watershed with randomly placed markers, a probability density function for object boundaries is created. In a second step, the algorithm then generates a meaningful segmentation of the image using this probability density function. The method performs best when the image contains regions of similar size, since it tends to break up larger regions and merge smaller ones. We propose two simple modifications that greatly improve the properties of the stochastic watershed: (1) add noise to the input image at every iteration, and (2) distribute the markers using a randomly placed grid. The noise strength is a new parameter to be set, but the output of the algorithm is not very sensitive to this value. In return, the output becomes less sensitive to the two parameters of the standard algorithm. The improved algorithm does not break up larger regions, effectively making the algorithm useful for a larger class of segmentation problems.
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| 12. |
- Blamey, Ben, et al.
(författare)
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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
- 2021
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Ingår i: GigaScience. - : Oxford University Press. - 2047-217X. ; 10:3, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.
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| 13. |
- Borgefors, Gunilla, et al.
(författare)
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CBA Annual Report 2002
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)
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| 14. |
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| 15. |
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| 16. |
- Bylow, Erik, et al.
(författare)
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Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models
- 2019
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Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 261-274
-
Konferensbidrag (refereegranskat)abstract
- In recent years, great progress has been made on the problem of 3D scene reconstruction using depth sensors. On a large scale, these reconstructions look impressive, but often many fine details are lacking due to limitations in the sensor resolution. In this paper we combine two well-known principles for recovery of 3D models, namely fusion of depth images with photometric stereo to enhance the details of the reconstructions. We derive a simple and transparent objective functional that takes both the observed intensity images and depth information into account. The experimental results show that many details are captured that are not present in the input depth images. Moreover, we provide a quantitative evaluation that confirms the improvement of the resulting 3D reconstruction using a 3D printed model.
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| 17. |
- Christersson, Albert, et al.
(författare)
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Comparison of 2D radiography and a semi-automatic CT-based 3D method for measuring change in dorsal angulation over time in distal radius fractures
- 2016
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Ingår i: Skeletal Radiology. - : Springer Science and Business Media LLC. - 0364-2348 .- 1432-2161. ; 45:6, s. 763-769
-
Tidskriftsartikel (refereegranskat)abstract
- Objective The aim of the present study was to compare the reliability and agreement between a computer tomography-based method (CT) and digitalised 2D radiographs (XR) when measuring change in dorsal angulation over time in distal radius fractures. Materials and methods Radiographs from 33 distal radius fractures treated with external fixation were retrospectively analysed. All fractures had been examined using both XR and CT at six times over 6 months postoperatively. The changes in dorsal angulation between the first reference images and the following examinations in every patient were calculated from 133 follow-up measurements by two assessors and repeated at two different time points. The measurements were analysed using Bland-Altman plots, comparing intra- and inter-observer agreement within and between XR and CT. Results The mean differences in intra- and inter-observer measurements for XR, CT, and between XR and CT were close to zero, implying equal validity. The average intra- and inter-observer limits of agreement for XR, CT, and between XR and CT were +/- 4.4 degrees, +/- 1.9 degrees and +/- 6.8 degrees respectively. Conclusions For scientific purpose, the reliability of XR seems unacceptably low when measuring changes in dorsal angulation in distal radius fractures, whereas the reliability for the semi-automatic CT-based method was higher and is therefore preferable when a more precise method is requested.
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| 18. |
- Colomb-Delsuc, Mathieu, et al.
(författare)
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Assessment of the percentage of full recombinant adeno-associated virus particles in a gene therapy drug using CryoTEM
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:6
-
Tidskriftsartikel (refereegranskat)abstract
- In spite of continuous development of gene therapy vectors with thousands of drug candidates in clinical drug trials there are only a small number approved on the market today stressing the need to have characterization methods to assist in the validation of the drug development process. The level of packaging of the vector capsids appears to play a critical role in immunogenicity, hence an objective quantitative method assessing the content of particles containing a genome is an essential quality measurement. As transmission electron microscopy (TEM) allows direct visualization of the particles present in a specimen, it naturally seems as the most intuitive method of choice for characterizing recombinant adeno-associated virus (rAAV) particle packaging. Negative stain TEM (nsTEM) is an established characterization method for analysing the packaging of viral vectors. It has however shown limitations in terms of reliability. To overcome this drawback, we propose an analytical method based on CryoTEM that unambiguously and robustly determines the percentage of filled particles in an rAAV sample. In addition, we show that at a fixed number of vector particles the portion of filled particles correlates well with the potency of the drug. The method has been validated according to the ICH Q2 (R1) guidelines and the components investigated during the validation are presented in this study. The reliability of nsTEM as a method for the assessment of filled particles is also investigated along with a discussion about the origin of the observed variability of this method.
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| 19. |
- Coufalova, Eva, et al.
(författare)
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Low Voltage Mini TEM
- 2014
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Ingår i: Proceedings.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 20. |
- Flood, Gabrielle, et al.
(författare)
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Efficient Merging of Maps and Detection of Changes
- 2019
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Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 348-360
-
Konferensbidrag (refereegranskat)abstract
- With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.
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| 21. |
- Gu, Xuan, 1988-, et al.
(författare)
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Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
- 2019
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Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030202040 - 9783030202057 ; 11482 LNCS, s. 489-498
-
Konferensbidrag (refereegranskat)abstract
- Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.
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| 22. |
- Gupta, Ankit
(författare)
-
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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| 23. |
- Gupta, Anindya, et al.
(författare)
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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591254 ; , s. 407-418
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Konferensbidrag (refereegranskat)abstract
- Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.
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| 24. |
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| 25. |
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| 26. |
- Gupta, Anindya, et al.
(författare)
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Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
- 2020
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Ingår i: International journal of imaging systems and technology (Print). - : Wiley. - 0899-9457 .- 1098-1098. ; 30:2, s. 327-339
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Tidskriftsartikel (refereegranskat)abstract
- Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme.
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| 27. |
- Gupta, Ankit, et al.
(författare)
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Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields
- 2024
-
Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 151
-
Tidskriftsartikel (refereegranskat)abstract
- Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.
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| 28. |
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| 29. |
- Gupta, Ankit, et al.
(författare)
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Is brightfield all you need for MoA prediction?
- 2022
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Konferensbidrag (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, and 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 correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.
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| 30. |
- Gupta, Ankit, et al.
(författare)
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SimSearch : A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images
- 2022
-
Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 26:8, s. 4079-4089
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Large-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments. Methods: The user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches. Results: The framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction. Conclusions: SimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction. Significance: SimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.
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| 31. |
- Gupta, Ankit, et al.
(författare)
-
Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks
- 2022
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Konferensbidrag (refereegranskat)abstract
- Attention Branch Networks (ABNs) have been shown to simultaneously provide visual explanation and improve the performance of deep convolutional neural networks (CNNs). In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance. We evaluate MSABN on benchmark image recognition and fine-grained recognition datasets where we observe MSABN outperforms ABN and baseline models. We also introduce a new data augmentation strategy utilizing the attention maps to incorporate human knowledge in the form of bounding box annotations of the objects of interest. We show that even with a limited number of edited samples, a significant performance gain can be achieved with this strategy.
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| 32. |
- Harrison, Philip John, et al.
(författare)
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Evaluating the utility of brightfield image data for mechanism of action prediction
- 2023
-
Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:7
-
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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| 33. |
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| 34. |
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| 35. |
- Hast, Anders, et al.
(författare)
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An efficient descriptor based on radial line integration for fast non invariant matching and registration of microscopy images
- 2017
-
Ingår i: Advanced Concepts for Intelligent Vision Systems. - Cham : Springer. - 9783319703527 ; , s. 723-734
-
Konferensbidrag (refereegranskat)abstract
- Descriptors such as SURF and SIFT contain a framework for handling rotation and scale invariance, which generally is not needed when registration and stitching of images in microscopy is the focus. Instead speed and efficiency are more important factors. We propose a descriptor that performs very well for these criteria, which is based on the idea of radial line integration. The result is a descriptor that outperforms both SURF and SIFT when it comes to speed and the number of inliers, even for rather short descriptors.
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| 36. |
- Hauser, Janosch, et al.
(författare)
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A microfluidic device for TEM sample preparation
- 2020
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Ingår i: Lab on a Chip. - : Royal Society of Chemistry. - 1473-0197 .- 1473-0189. ; 20:22, s. 4186-4193
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Tidskriftsartikel (refereegranskat)abstract
- Transmission electron microscopy (TEM) allows for visualizing and analyzing viral particles and has become a vital tool for the development of vaccines and biopharmaceuticals. However, appropriate TEM sample preparation is typically done manually which introduces operator-based dependencies and can lead to unreliable results. Here, we present a capillary-driven microfluidic single-use device that prepares a TEM grid with minimal and non-critical user interaction. The user only initiates the sample preparation process, waits for about one minute and then collects the TEM grid, ready for imaging. Using Adeno-associated virus (AAV) particles as the sample and NanoVan (R) as the stain, we demonstrate microfluidic consistency and show that the sample preparation quality is sufficient for automated image analysis. We further demonstrate the versatility of the microfluidic device by preparing two protein complexes for TEM investigations using two different stain types. The presented TEM sample preparation concept could alleviate the problems associated with human inconsistency in manual preparation protocols and allow for non-specialists to prepare TEM samples.
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| 37. |
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| 38. |
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Image Analysis
- 2019
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Proceedings (redaktörskap) (refereegranskat)abstract
- This volume constitutes the refereed proceedings of the 21st Scandinavian Conference on Image Analysis, SCIA 2019, held in Norrköping, Sweden, in June 2019.The 40 revised papers presented were carefully reviewed and selected from 63 submissions. The contributions are structured in topical sections on Deep convolutional neural networks; Feature extraction and image analysis; Matching, tracking and geometry; and Medical and biomedical image analysis.
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| 39. |
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| 40. |
- Kylberg, Gustaf, 1983-
(författare)
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Automatic Virus Identification using TEM : Image Segmentation and Texture Analysis
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks.The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detected in EM images while being unrecognizable by molecular methods.One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number.This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification.One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context.The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.
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| 41. |
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| 42. |
- Kylberg, Gustaf, et al.
(författare)
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Detecting Virus-like Particles Using Transmission Electron Microscopy
- 2009
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Ingår i: Proceedings SSBA 2009, Symposium on Image Analysis. - Halmstad : EIS, Halmstad University. - 9789163339240 ; , s. 13-16
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a multi scale approach for automating the image acquisition process for an computerized virus diagnostic application. Our methods are designed to mimic the methodology used by virus TEM experts manually operating the microscope. The methods decrease the search area considerably. In addition we present a segmentation method for virus-like particles based on local intensity information and PCA. This method makes no assumption regarding shape which is vital since many viruses are highly pleomorphic, i.e., have different shapes. The only input parameter used is the approximate virus thickness, which is a conserved feature within a virus species.
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| 43. |
- Kylberg, Gustaf, et al.
(författare)
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Evaluation of noise robustness for local binary pattern descriptors in texture classification
- 2013
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Ingår i: EURASIP Journal on Image and Video Processing. - : Springer. - 1687-5176 .- 1687-5281. ; :17
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Tidskriftsartikel (refereegranskat)abstract
- Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.
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| 44. |
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| 45. |
- Kylberg, Gustaf, et al.
(författare)
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Local Intensity and PCA Based Detection of Virus Particle Candidates in Transmission Electron Microscopy Images
- 2009
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Ingår i: Proc. 6th International Symposium on Image and Signal Processing and Analysis. - Piscataway, NJ : IEEE. - 9789531841351 ; , s. 426-431
-
Konferensbidrag (refereegranskat)abstract
- We present a general method using local intensity informationand PCA to detect objects characterized onlyby that they differ from their surroundings. We apply ourmethod to the problem of automatically detecting virus particlecandidates in transmission electron microscopy images.Viruses have very different shapes and sizes, manyspecies are spherical whereas others are highly pleomorphic.To detect any kind of virus particles in electron microscopyimages it is therefore necessary to use a methodnot restricted to detection of a specific shape. The methodproposed here uses only one input parameter, the approximatevirus thickness, which is a conserved feature withina virus species. It is capable to detect virus particles ofvery varying shapes. Results on images with highly texturedbackground of several different virus species are presented.
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| 46. |
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| 47. |
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| 48. |
- Kylberg, Gustaf, et al.
(författare)
-
Segmentation of virus particle candidates in transmission electron microscopy images
- 2012
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Ingår i: Journal of Microscopy. - : Blackwell Publishing. - 0022-2720 .- 1365-2818. ; 245:2, s. 140-147
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.
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| 49. |
- Kylberg, Gustaf, et al.
(författare)
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Towards Automated TEM for Virus Diagnostics : Segmentation of Grid Squares and Detection of Regions of Interest
- 2009
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Ingår i: Proceedings of the 16th Scandinavian Conference on Image Analysis (SCIA). - Berlin : Springer-Verlag. - 9783642022296 ; , s. 169-178, s. 169-178
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Konferensbidrag (refereegranskat)abstract
- When searching for viruses in an electron microscope thesample grid constitutes an enormous search area. Here, we present methodsfor automating the image acquisition process for an automatic virusdiagnostic application. The methods constitute a multi resolution approachwhere we first identify the grid squares and rate individual gridsquares based on content in a grid overview image and then detect regionsof interest in higher resolution images of good grid squares. Our methodsare designed to mimic the actions of a virus TEM expert manually navigatingthe microscope and they are also compared to the expert’s performance.Integrating the proposed methods with the microscope wouldreduce the search area by more than 99.99% and it would also removethe need for an expert to perform the virus search by the microscope.
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| 50. |
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