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State of the Art Related to Image Analysis of Living Cells
In biological image analysis, one of the major challenges is related to the type of modalities and cells to be observed [115, 70]. This is similar to medical image analysis with all the different imaging modalities like MRI, CT, etc. and the different tissue and organs to be observed. The two main families of observation in cell biology are fluorescence, that include the confocal microscope and non-fluorescence, that include phase contrast and Differential Interference Contrast (DIC) microscope. In phase contrast and DIC images, the cell does not always appear as being very con- trasted, compared to the background, but more like a region with different texture and non-uniform intensity. Beside, the images contain several artefacts, depending on their position with regards to the microscope objectives and the light orientation, such as the phase halo and out-of-focus blur. Such effects are difficult to reproduce as they are very dependent of the cell type and the parameters of the microscope. In fluorescence, it is actually possible to dye specific cellular structure like the nuclei or the membrane of the cells. The marker is made to target and dye only a specific structure of the cell, giving the assurance of what is observed. This is time consuming as the dye must get in the cells and attach itself to the specific structure, but it fully highlights the target structure and the cells, even if they are deep in a multilayer tissue. As the micro- scope will detect the fluorescence at a precise wavelength and discard any other sources of light, the fluorescence images do not contain any artefacts or background. The intensity at a point only corresponds to the concentration of fluorescence markers at that point. It is also possible to have multiple fluorescence markers on one image, to highlight different structures.
Depending on which type of modality the image was taken, even if the objectives are similar, the methods, parametrisations and results can be very different. Most research use fluorescence modalities as they present more reliable and cleaner data to work on, but the time needed for marking the cells, the cost of the markers to be used, and the high intensity of the laser used in fluorescence imaging are high requirement that are not always easy to meet for certain type of cells and certain type of experiments.
Cell Segmentation
The first step of every cell analysis is to detect and/or segment the cell present in the image, and to do so with an automatic approach. It is a wide field of research and a lot has been done on the subject, and an important number of methods exist [104]. Intensity Threshold is one of the most common approaches widely used in generic image segmentation [90]. In cell segmentation, it makes the assumption that the cells are highly contrasted with the background. In this case, a global or a local threshold determined from the histogram of the image can provide good results in an efficient and simple way. On non-fluorescence image, this will allow to detected contrasted object present in the image (Fig. 2-1d), and for fluorescence image, this will detect the cellular structure that has been marked (Fig. 2-1f). However the results are too dependent from the image contrast and its illumination and, if used alone, can produce poor segmentation results. The cells may not be completely segmented due to non-uniform intensity of the pixels of the cells but only part of the cells are segmented (Fig. 2-1e), and touching cells may be detected as one large cell, degrading the segmentation precision (Fig. 2-1f). Therefore, pre-processing such as illumination correction, histogram equalisation and noise reduction are often used before any intensity threshold, and post-processing using morphological filter such as opening, closing and hole filling, usually follow the threshold to remove noise, smooth the detection, separate lightly connected cells and overall improve the final detection 2-1g. Such method was used by Al-Kofahi et.al. [2] as a cell detection step in phase contrast images. This is a good method for simple case, were the cells are not touching each other and the contrast in the images is high, but may be insufficient for more complex images. A variance of thresholding approach is local thresholding. Usually much easier to adapt, the intensity values, means and variance are observed on a smaller patch of the image, usually containing a cell to be segmented, and the threshold is determined on the patch only [103, 3, 6].
Cell Fate Prediction
Existing cell fate prediction methods treat simple cases – by determining if the cells are going to live, die, or divide [2, 44, 43] – , for advanced cases – by finding out if their evolution is affected by drugs or not [5] – , and finally, for complicated cases (e.g. with stem cells) – by anticipating if the cells are going to differentiate themselves and if so, into which cell type [21]. The approaches are based on feature extraction, usually done during the segmentation and tracking process. The features on which the prediction is based, depend of the type of modality, as they allow observing different processes characterising the cells behaviour. For example, on time lapse sequences, the cells movement is a key parameter to most of the existing process. Both the global movement, defined by the distance travelled by the cell from the beginning of the sequence, and the local movement, defined by the distance travelled by the cell between two time step, are monitored. The shape takes also an important role, usually observed through the eccentricity and the size of the cell. Fate prediction does not only apply to single cells, but also to tissue or structure formation, in which case, the time between division and the time taken for a division play an important role [102]. An important phase of learning is needed to train the process to recognise the different patterns in the features. Doing so, requires a consistent ground truth. Coupled with a tracking and tracing process, fate prediction becomes a powerful tool for studying cells [101].
Neurosphere tracking
Neurospheres are living objects in a moving environment. From the first neural stem cell to a large structure, the neurosphere will continually move in the dish. The movement is mostly the effect of cells movement in the suspension liquid, and also possible liquid current inside the dish, and the neurosphere would often go out of the field of view of the microscope. Therefore, the first important step in our study was to track the neurosphere in the time-lapse sequence. In order to do it, we use the » − # motion filter [65], a back- ground estimator for motion detection. This estimator only uses time efficient operations that can be easily embedded for real-time applications. The use of a background estimator was decided over the observation that cells and neurospheres are living body in movement, in opposition with dead cells and dead neurospheres, which lose all motion. The main idea of the algorithm, is to compare the image difference #t, obtained by the subtraction between the current image It and a mean image Im. The variance V of the sequence gives the temporal activity of every pixel in the image over time. The more a pixel changes its value, the more active it is considered. If a pixel has a higher #t value than is current temporal activity, then it is likely to be part of the foreground as a moving object. This threshold discards small and regular movement in the background such as possible small dust. The strength of this method is its robustness to ghost and trace effects that can appear in the motion detection process. In order to eliminate these effects, a hybrid reconstruction (HRec), based on the so-called forgetting morphological operator erosion and dilatation [86], is applied on the gradient norm image of #t and exponentially eliminates ghosts while it goes through the sequence. The overall process iterates over each new time step, updating the different value according to the new acquisitions as follow.
Cell Detection in Neurosphere Cluster
The microscope observation contains two types of objects to detect and segment, each at different level: the neurosphere and the cells in the neurosphere. With the exception of dead cells or external cells, all the cells in which we are interested are part of the neurosphere, and it is the accumulation of those cells that form the neurosphere structure. Therefore, we aim to first detect and segment the neurosphere in the image, and then detect and segment the cells that are contained in the neurosphere. To do so, we first apply a restoration process on the image to remove unwanted artefacts contained into phase contrast images.
Then, we use a Level Set method to segment and detect the neurosphere using the image gradient, followed by a circle fitting detection method using the partial circles from the boundary of the neurosphere. Finally, we apply a post process to keep, reject or merge the detections.
Phase Contrast Restoration
Living cells can be imaged by several different image modality, DIC, phase contrast, con- focal, etc. that impact on which methods that are used and how they are used. Even in the same modality, like phase contrast. In our case, the images vary in term of contrast, depending on positive or negative phase contrast, illumination, phase contrast artefacts, etc.. This brings several problems in term of robustness of the segmentation and the detection methods. Yin et.al. propose to overcome this aspect by presenting the phase contrast image as an image degraded by the phase contrast modality and by restoring the image using the phase contrast microscope physics. This restoration aim to create an artefact-free image, more appropriate for image processing.
Table of contents :
Acknowledgments
Abstract
Author’s Publications
List of Figures
List of Tables
Acronyms
Introduction
Intelligent Visual System for Neural Stem Cell Observation
Organisation of the Thesis
1 Biological Context Related to Neural Stem Cells
1.1 Stem Cells
1.1.1 Progenitor Cells
1.1.2 Neural Stem Cells
1.2 Cell Culture and Experimentation
1.2.1 Adhesive and Floating Culture
1.2.2 Neurosphere Formation Assay
1.2.3 Neurosphere
1.2.4 Imaging of Neurosphere
Observation Through Confocal Microscope
Observation Through Phase-Contrast Microscope
1.2.5 Experimental Limitations
1.2.6 Data
Training Data Set
Test Data Set
1.3 Challenges and Perspectives
1.3.1 Biological
1.3.2 Computational
1.3.3 Scope and Objectives
1.4 Overview
1.4.1 Analysis
1.4.2 Synthesis
1.4.3 Selection
1.4.4 Contribution
2 Analysis: Cell Detection under Phase Contrast
2.1 Introduction
2.2 State of the Art Related to Image Analysis of Living Cells
2.2.1 Fluorescence and Non-Fluorescence Cells
2.2.2 Cell Segmentation
2.2.3 Cell Tracking
2.2.4 Cell Fate Prediction
2.3 Neurosphere tracking
2.4 Cell Detection in Neurosphere Cluster
2.4.1 Phase Contrast Restoration
2.4.2 Partial Circle Detection
Region of Interest
Neurosphere Boundary Extraction
Candidate Circle Fitting
Circle Selection
2.5 Results and Analysis
2.5.1 Neurosphere Tracking
2.5.2 Cell Detection
2.6 Conclusion
3 Synthesis: NeurosphereModelGeneration
3.1 Introduction
3.2 Using Biological Prior Knowledge
3.2.1 Structural and Topological Constrains
3.2.2 Neurosphere Proliferation Speed
3.2.3 Phase Contrast Halo Artefact
3.3 Random Sphere Approach
3.4 Evolution Algorithm Approach
3.4.1 Selection Operation
3.4.2 Mutation Operation
3.4.3 Mating Operation
3.4.4 Algorithm GPU Design
3.4.5 Evolution Algorithm Test
3.5 Iterative Mesh Approach
3.5.1 Quad Edge Mesh Structure
3.5.2 Incremental Delaunay Triangulation
Initialisation
Main algorithm
Primal – Dual
3.5.3 Division Process
3.5.4 Depth and Layers
Depth
Layer
3.5.5 Iterative Mesh Test
3.6 Conclusion
4 Selection: 3-Dto 2-DRegistration
4.1 Introduction
4.2 State of the Art Related to Image Registrationt
4.2.1 Evaluation Function
Area-Based Methods
Feature-Based Methods
4.2.2 Transform
Rigid Transformation
Non-rigid Transformation
4.2.3 Optimisation
4.2.4 3-D to 2-D Registration Strategy
Projection Strategy
Back-Projection Strategy
Reconstruction Strategy
4.3 Adapting Registration for Neurosphere Model Selection
4.3.1 Projection
4.3.2 Evaluation Function
4.3.3 Transform
4.3.4 Optimisation
4.3.5 Visualisation
4.4 Results and Analysis
4.4.1 Evolution Process
Over a Sequence
At Precise Time Step
4.4.2 Iterative Mesh Process
Over a Sequence
At Precise Time Step
4.4.3 Parallel Model Synergism
4.5 Conclusion
Conclusion and Outlooks
Conclusion
Outlooks
Bibliography
