A quantitative image analysis for the cellular cytoskeleton during in vitro tumor growth

The cellular cytoskeleton is a dynamic subcellular structure that can be a marker of key biological phenomena including cell division, organelle movement, shape changes and locomotion during the avascular tumor phase. Little attention is paid to quantify changes in the cytoskeleton while nuclei and cytoplasmic both are present in subcellular microscopic images. In this paper, we proposed a quantitative image analysis method to analyze subcellular cytoskeletal changes using a texture analysis method preceded by segmentation of nuclei, cytoplasm and ruffling regions (area except nuclei and cytoplasm). To test and validate this model we hypothesized that Mammary Serine Protease Inhibitor (maspin) acts as cytoskeleton regulator that mediates cell-extracellular matrix (ECM) adhesion in tumor. Maspin-a tumor suppressor gene shows multiple tumor suppressive properties such as increasing tumor cell apoptosis and reducing migration, proliferation, invasion, and overall tumor metastasis. The proposed method obtained separated ruffling regions from segmentation steps and then adopted gray–level histograms (GLH) and grey-level co-occurrence matrix (GLCM) texture analysis techniques. In order to verify the reliability, the proposed texture analysis method was used to compare the control and maspin expressing cells grown on different ECM components: plastic, collagen I, fibronectin and laminin. The results show that the texture parameters extracted reflect the different cytoskeletal changes. These changes indicate that maspin acts as a regulator of the cell-ECM enhancement process, while it reduces the cell migration. Overall, this paper not only presents a quantitative image analysis approach to analyze subcellular cytoskeletal architectures but also provides a comprehensive tool for the biologist, pathologist, cancer specialist, and computer scientist to understand cellular and subcellular organization of cells. In long term, this method can be extended to be used in live cell tracking in vivo, image informatics based point-of-care expert system and quantification of various complex architectures in organisms

Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set

An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells

In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree of overlap among cells, the poor contrast of cell cytoplasm and the presence of mucus, blood, and inflammatory cells. Our methodology addresses these issues by utilizing a joint optimization of multiple level set functions, where each function represents a cell within a clump, that have both unary (intracell) and pairwise (intercell) constraints. The unary constraints are based on contour length, edge strength, and cell shape, while the pairwise constraint is computed based on the area of the overlapping regions. In this way, our methodology enables the analysis of nuclei and cytoplasm from both free-lying and overlapping cells. We provide a systematic evaluation of our methodology using a database of over 900 images generated by synthetically overlapping images of free-lying cervical cells, where the number of cells within a clump is varied from 2 to 10 and the overlap coefficient between pairs of cells from 0.1 to 0.5. This quantitative assessment demonstrates that our methodology can successfully segment clumps of up to 10 cells, provided the overlap between pairs of cells is <0.2. Moreover, if the clump consists of three or fewer cells, then our methodology can successfully segment individual cells even when the overlap is ∼0.5. We also evaluate our approach quantitatively and qualitatively on a set of 16 extended depth of field images, where we are able to segment a total of 645 cells, of which only ∼10% are free-lying. Finally, we demonstrate that our method of cell nuclei segmentation is competitive when compared with the current state of the art.Zhi Lu, Gustavo Carneiro, and Andrew P. Bradle

Automated Morphology Analysis of Nanoparticles

The functional properties of nanoparticles highly depend on the surface morphology of the particles, so precise measurements of a particle's morphology enable reliable characterizing of the nanoparticle's properties. Obtaining the measurements requires image analysis of electron microscopic pictures of nanoparticles. Today's labor-intensive image analysis of electron micrographs of nanoparticles is a significant bottleneck for efficient material characterization. The objective of this dissertation is to develop automated morphology analysis methods. Morphology analysis is comprised of three tasks: separate individual particles from an agglomerate of overlapping nano-objects (image segmentation); infer the particle's missing contours (shape inference); and ultimately, classify the particles by shape based on their complete contours (shape classification). Two approaches are proposed in this dissertation: the divide-and-conquer approach and the convex shape analysis approach. The divide-and-conquer approach solves each task separately, taking less than one minute to complete the required analysis, even for the largest-sized micrograph. However, its separating capability of particle overlaps is limited, meaning that it is able to split only touching particles. The convex shape analysis approach solves shape inference and classification simultaneously for better accuracy, but it requires more computation time, ten minutes for the biggest-sized electron micrograph. However, with a little sacrifice of time efficiency, the second approach achieves far superior separation than the divide-and-conquer approach, and it handles the chain-linked structure of particle overlaps well. The capabilities of the two proposed methods cannot be substituted by generic image processing and bio-imaging methods. This is due to the unique features that the electron microscopic pictures of nanoparticles have, including special particle overlap structures, and large number of particles to be processed. The application of the proposed methods to real electron microscopic pictures showed that the two proposed methods were more capable of extracting the morphology information than the state-of-the-art methods. When nanoparticles do not have many overlaps, the divide-and-conquer approach performed adequately. When nanoparticles have many overlaps, forming chain-linked clusters, the convex shape analysis approach performed much better than the state-of-the-art alternatives in bio-imaging. The author believes that the capabilities of the proposed methods expedite the morphology characterization process of nanoparticles. The author further conjectures that the technical generality of the proposed methods could even be a competent alternative to the current methods analyzing general overlapping convex-shaped objects other than nanoparticles

Data Science Methods for Analyzing Nanomaterial Images and Videos

A large amount of nanomaterial characterization data has been routinely collected by using electron microscopes and stored in image or video formats. A bottleneck in making effective use of the image/video data is the lack of the development of sophisticated data science methods capable of unlocking valuable material pertinent information buried in the raw data. To address this problem, the research of this dissertation begins with understanding the physical mechanisms behind the concerned process to determine why the generic methods fall short. Afterwards, it designs and improves image processing and statistical modeling tools to address the practical challenges. Specifically, this dissertation consists of two main tasks: extracting useful information from images or videos of nanomaterials captured by electron microscopes, and designing analytical methods for modeling/monitoring the dynamic growth of nanoparticles. In the first task, a two-pipeline framework is proposed to fuse two kinds of image information for nanoscale object detection that can accurately identify and measure nanoparticles in transmission electron microscope (TEM) images of high noise and low contrast. To handle the second task of analyzing nanoparticle growth, this dissertation develops dynamic nonparametric models for time-varying probability density functions (PDFs) estimation. Unlike simple statistics, a PDF contains fuller information about the nanoscale objects of interests. Characterizing the dynamic changes of the PDF as the nanoparticles grow into different sizes and morph into different shapes, the proposed nonparametric methods are capable of analyzing an in situ TEM video to delineate growth stages in a retrospective analysis, or tracking the nanoparticle growth process in a prospective analysis. The resulting analytic methods have applications in areas beyond the nanoparticle growth process such as the image-based process control tasks in additive manufacturing