Unsupervised Segmentation and Statistical Shape Analysis for PanVision

PanVision 的无监督分割和统计形状分析

• 批准号: 2873400
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2873400
• 关键词: Unsupervised; Segmentation; Statistical; Shape; Analysis

PanVision is a new microscopy technique that uses light microscopy to provide images of cells with a resolution comparable to electron microscopy (EM). It builds on expansion microscopy and can be used both to make the whole proteome of a cell visible but also in combination with different antibody staining to identify specific structures within the cell. The image acquisition is fast, and compared to EM, the preparation of samples is simpler, and an ordinary confocal microscope can be used instead of specialised equipment. This will enable researchers to investigate cellular ultrastructures and the distribution of proteins within the cell more easily. To assist in this process, I will build on recent advances in computer vision to develop unsupervised segmentation methods that identify these structures automatically, and I am going to apply statistical analyses to quantify the shapes and distribution of objects within the resulting segmentations.My work will be structured into the following major aims:Aim 1: Unsupervised segmentation. While unsupervised segmentation methods are currently a very active field of research in computer vision, these new approaches are mainly developed for natural images. Therefore, the first step of this project is to adapt state-of-the-art unsupervised segmentation methods for images produced by PanVision. To do so, I am going to investigate which components of current approaches are most susceptible to the challenges coming with microscopy data like a high noise to signal ratio and low contrast, and I am going to research how to adapt the loss functions and architectures to make them more robust.Aim 2: Incorporating prior knowledge. Moreover, most new unsupervised segmentation methods are developed for images of a wide range of different contexts and therefore do not take specific prior knowledge into account. However, in this application, we have a rough idea of the resulting cluster sizes and know that we can expect a high degree of self-similarity between the cellular ultrastructures. Therefore, I am going to research ways to incorporate this prior shape information into the segmentation algorithms.Aim 3: Statistical shape analysis. Finally, I will use the resulting segmentation masks both in 2D and 3D to analyse the shape [9, 10] and distribution of different structures within the cell-like mitochondria. Moreover, it was possible to prove that our methods work for HeLa cells. I aim to apply the developed techniques to tissues in different biological contexts.In this project, I will closely collaborate with the group led by Joerg Bewersdorf (Yale University, New Haven, USA), who is developing the PanVision protocol. There is already sufficient data available for Aim 1 and 2, and additional images are in process as well. Additional collaborations with the St Johnston Lab (University of Cambridge) to apply this imaging protocol to intact tissues are planned. This work will enhance unsupervised segmentation algorithms to make them more reliable in the context of microscopy data. Additionally, I aim to enable new biological discoveries and enhance the understanding of fundamental biological processes by providing imaging pipelines that allow the automatic segmentation and shape analysis of cellular ultrastructures. This project falls within the EPSRC Healthcare Technologies/medical imaging research area.

PanVision是一种新的显微镜技术，使用光学显微镜提供细胞图像，其分辨率可与电子显微镜（EM）相媲美。它建立在扩增显微镜的基础上，既可以用于使细胞的整个蛋白质组可见，也可以与不同的抗体染色相结合，以识别细胞内的特定结构。图像采集速度快，与EM相比，样品的制备更简单，可以使用普通的共聚焦显微镜代替专门的设备。这将使研究人员能够更容易地研究细胞超微结构和细胞内蛋白质的分布。为了帮助这个过程，我将在计算机视觉的最新进展的基础上开发自动识别这些结构的无监督分割方法，我将应用统计分析来量化分割结果中对象的形状和分布。我的工作将分为以下主要目标：目标1：无监督分割。虽然无监督分割方法目前是计算机视觉中非常活跃的研究领域，但这些新方法主要是针对自然图像开发的。因此，该项目的第一步是为PanVision产生的图像采用最先进的无监督分割方法。为此，我将研究当前方法的哪些组件最容易受到显微镜数据带来的挑战（如高信噪比和低对比度）的影响，并研究如何调整损失函数和架构，使其更加鲁棒。目标2：简化先验知识。此外，大多数新的无监督分割方法是针对各种不同背景的图像开发的，因此没有考虑特定的先验知识。然而，在这个应用程序中，我们有一个粗略的想法，得到的集群大小，并知道我们可以预期的细胞超微结构之间的高度自相似性。因此，我将研究如何将这些先验形状信息融入到分割算法中。目的3：统计形状分析。最后，我将使用生成的2D和3D分割掩模来分析细胞状线粒体内不同结构的形状[9，10]和分布。此外，有可能证明我们的方法适用于HeLa细胞。我的目标是将开发的技术应用于不同生物背景下的组织。在这个项目中，我将与Joerg Bewersdorf（耶鲁大学，纽黑文，美国）领导的小组密切合作，他正在开发PanVision协议。目标1和目标2已经有足够的数据，其他图像也在处理中。计划与St约翰斯顿实验室（剑桥大学）进行额外合作，将该成像方案应用于完整组织。这项工作将增强无监督分割算法，使其在显微镜数据的背景下更加可靠。此外，我的目标是通过提供允许自动分割和细胞超微结构形状分析的成像管道来实现新的生物学发现并增强对基本生物学过程的理解。该项目属于EPSRC医疗保健技术/医学成像研究领域的福尔斯。