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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC Santa Barbara Electronic Theses and Dissertations (2022)

Time lapse 3D images are important resources for biology research because they notonly provide 3D structural information but also provide temporal information. Much of the analysis of these data are manual and subjective, and does not scale well with the large amount of imaging data that is routinely collected these days. This is especially true for 3D and time-lapse imagery. Fundamental problems such as detecting cells, subcellular features, tracking of such structures in 3D over time, and modeling 3D shapes in robust manner, remain. The problems addressed in this dissertation are motivated by two bio-imaging problems:

1. Understanding the plant pavement cell growth pattern. The pavement cell growthcontrols the leaf expansion patterns and rates which are the key determinants of the overall photosynthetic rates of the canopy. Time lapse image stacks from 3D confocal imagery are a good resource to study the pavement cell growth process. To better understand this process, machine learning methods are developed to detect and track cellular and sub-cellular features. We developed a deep learning enabled time-lapse 3D analysis pipeline that includes novel boundary tagged 3D segmentation method, and a graph based sub-cellular feature extraction and tracking method. Detailed quantitative evaluation results demonstrate the robustness and state-of-the art performance of the proposed methods.

2. Neuron morphology analysis. Cell morphology especially neuron morphology playsan important role in biology because neuron functions are closely related to neuron morphology. In this dissertation, we propose a robust computational 3D skeleton model to analyze neuron morphology. It is the first deep learning method to compute a 3D neuron skeleton model directly from discrete 3D surface points for neuron classification. The main innovation is in formulating the learning problem associated with computing the medial axis transform that represents the 3D skeleton. We apply our method on two Datasets, Ciona neuron dataset and C.elegan neuron dataset for classification. It results in an accurate and robust skeleton representation, and achieves state-of-the-art performance in classifying neuron types.

The implementation of the methods developed above and the associated data aremade available on GitHub and also as a software service through the UCSB BisQue platform.

    Cover page: Segmentation, Tracking, and Shape Modeling for 3D Time-lapse Microscopy Images
    • Article
    • Peer Reviewed
    UC Santa Barbara Previously Published Works (2023)
    We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing curve skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.
      Cover page: A robust approach to 3D neuron shape representation for quantification and classification.Creative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Santa Barbara Previously Published Works (2023)
      We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing"curve"skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.
        Cover page: 3D Neuron Morphology AnalysisCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Santa Barbara Previously Published Works (2019)
        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 1.
          Cover page: Accurate 3D Cell Segmentation Using Deep Features and CRF Refinement
          • Article
          • Peer Reviewed
          UC Santa Barbara Previously Published Works (2020)
          [This corrects the article DOI: 10.3389/fnins.2019.01449.].
            Cover page: Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location InformationCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Santa Barbara Previously Published Works (2020)
            The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks.
              Cover page: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location InformationCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Santa Barbara Previously Published Works (2023)
              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.
                Cover page: Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse imagesCreative Commons 'BY' version 4.0 license
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