Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding of cell morphogenesis. Towards this, we outline a cell segmentation method that uses a deep neural network architecture to extract a confidence map of cell boundaries, followed by a 3D watershed algorithm and a final refinement using a conditional random field. In addition to improving the accuracy of segmentation compared to other state-of-the-art methods, the proposed approach also generalizes well to different datasets without the need to retrain the network for each dataset. Detailed experimental results are provided, and the source code is available on GitHub.Comment: 5 pages, 5 figures, 3 table

Multivariate Analysis of Leaf Tissue Morphogenesis

Leaf size and shape are strongly influenced by the growth patterns of the epidermal tissue. Pavement cells are the prevalent cell type in the epidermis and during cell expansion they undergo a drastic shape change from a simple polyhedral cells to puzzled-shaped cell. The role of these cell protrusions, more commonly referred to as lobes, remains unknown but their formation has been proposed to help increase the structural integrity of the epidermal tissue. How the symmetry breaking event that initiates a lobe is controlled remains unknown, however pharmacological and genetic disruption of the microtubule system has been shown to interfere not only with lobe initiation but also with lobe expansion. Additionally, the role of microtubules in the pattering of microfibril deposition, the load-bearing structure of the cell wall, makes the microtubule system a good candidate to evaluate its dynamics as a function of shape change. Two main mechanical models for lobe initiation are evaluated here, one where microtubules serve as stable features suppressing local expansion and one where microtubules, similarly to the anisotropic expansion patterning in hypocotyl cells, promote the local anisotropic expansion of the cell resulting in lobe formation. The main method to evaluate these models was through the use of long-term time-lapse image analysis using a plasma-membrane marker for accurate shape change quantification and a microtubule marker to quantify their location, persistence, and density as a function of cell shape change. Using the junctions where three cells come together, cells were sub-divided into segments and the shape of these segments were tracked using a new coordinate system that allowed the detection of new lobes as which can arise from ∼300 deflections. By mapping sub-cellular processes, such as microtubule persistence, to this coordinate system, correlations of microtubule organization and shape change was possible. Additionally, a subset of microtubules bundles that splay across the anticlinal and periclinal walls, perpendicular and parallel to the leaf surface respectively, were identified as marking the location and direction of lobe formation. Disrupting the cell boundary by partially digesting pectin, a main component in the middle lamella, revealed the cell-autonomous morphogenesis mechanism in pavement cells. Under pectinase treatment, cell invaginations were produced and similarly to lobes their initiation was microtubule and cellulose dependent. Lastly, stress prediction using finite-element models, based from live-cell images, co-localized regions of high cell wall stress with both microtubule persistence and shape shape locations in both lobing and invaginated segments. Together, a model of cellular shape change is presented where microtubules translate cell wall stresses to tissue morphogenesis

Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal

CellECT: cell evolution capturing tool.

BackgroundRobust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance. Identification and correction of segmentation errors in time-series data is of prime importance for proper validation of the subsequent analysis. The primary contribution of this work is a novel method for interactive segmentation and analysis of microscopy data, which learns from and guides user interactions to improve overall segmentation.ResultsWe introduce an interactive cell analysis application, called CellECT, for 3D+t microscopy datasets. The core segmentation tool is watershed-based and allows the user to add, remove or modify existing segments by means of manipulating guidance markers. A confidence metric learns from the user interaction and highlights regions of uncertainty in the segmentation for the user's attention. User corrected segmentations are then propagated to neighboring time points. The analysis tool computes local and global statistics for various cell measurements over the time sequence. Detailed results on two large datasets containing membrane and nuclei data are presented: a 3D+t confocal microscopy dataset of the ascidian Phallusia mammillata consisting of 18 time points, and a 3D+t single plane illumination microscopy (SPIM) dataset consisting of 192 time points. Additionally, CellECT was used to segment a large population of jigsaw-puzzle shaped epidermal cells from Arabidopsis thaliana leaves. The cell coordinates obtained using CellECT are compared to those of manually segmented cells.ConclusionsCellECT provides tools for convenient segmentation and analysis of 3D+t membrane datasets by incorporating human interaction into automated algorithms. Users can modify segmentation results through the help of guidance markers, and an adaptive confidence metric highlights problematic regions. Segmentations can be propagated to multiple time points, and once a segmentation is available for a time sequence cells can be analyzed to observe trends. The segmentation and analysis tools presented here generalize well to membrane or cell wall volumetric time series datasets

CellECT: cell evolution capturing tool.

BackgroundRobust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance. Identification and correction of segmentation errors in time-series data is of prime importance for proper validation of the subsequent analysis. The primary contribution of this work is a novel method for interactive segmentation and analysis of microscopy data, which learns from and guides user interactions to improve overall segmentation.ResultsWe introduce an interactive cell analysis application, called CellECT, for 3D+t microscopy datasets. The core segmentation tool is watershed-based and allows the user to add, remove or modify existing segments by means of manipulating guidance markers. A confidence metric learns from the user interaction and highlights regions of uncertainty in the segmentation for the user's attention. User corrected segmentations are then propagated to neighboring time points. The analysis tool computes local and global statistics for various cell measurements over the time sequence. Detailed results on two large datasets containing membrane and nuclei data are presented: a 3D+t confocal microscopy dataset of the ascidian Phallusia mammillata consisting of 18 time points, and a 3D+t single plane illumination microscopy (SPIM) dataset consisting of 192 time points. Additionally, CellECT was used to segment a large population of jigsaw-puzzle shaped epidermal cells from Arabidopsis thaliana leaves. The cell coordinates obtained using CellECT are compared to those of manually segmented cells.ConclusionsCellECT provides tools for convenient segmentation and analysis of 3D+t membrane datasets by incorporating human interaction into automated algorithms. Users can modify segmentation results through the help of guidance markers, and an adaptive confidence metric highlights problematic regions. Segmentations can be propagated to multiple time points, and once a segmentation is available for a time sequence cells can be analyzed to observe trends. The segmentation and analysis tools presented here generalize well to membrane or cell wall volumetric time series datasets