Unsupervised Segmentation and Statistical Shape Analysis for PanVision

PanVision是一种新的显微技术，它使用光学显微镜来提供与电子显微镜(EM)相当的分辨率的细胞图像。它建立在扩张显微镜的基础上，既可以用来使细胞的整个蛋白质组可见，也可以结合不同的抗体染色来识别细胞内的特定结构。图像采集速度快，与EM相比，样品制备更简单，可以用普通的共焦显微镜代替专用设备。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。为了帮助这一过程，我将以计算机视觉的最新进展为基础，开发自动识别这些结构的非监督分割方法，并将应用统计分析来量化所产生的分割中对象的形状和分布。我的工作将被组织为以下主要目标：目标1：非监督分割。虽然非监督分割方法是当前计算机视觉中一个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，本项目的第一步是将最先进的非监督分割方法应用于PanVision产生的图像。为此，我将调查当前方法的哪些组件最容易受到高信噪比和低对比度等显微镜数据挑战的影响，并将研究如何调整损耗函数和体系结构，使其更加健壮。目标2：纳入先前的知识。此外，大多数新的非监督分割方法都是针对大范围不同背景的图像而开发的，因此没有考虑特定的先验知识。然而，在这一应用中，我们对结果簇的大小有一个大致的概念，并知道我们可以预期细胞超结构之间具有高度的自相似性。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。目标3：统计形状分析。最后，我将使用得到的2D和3D分割模板来分析细胞样线粒体中不同结构的形状[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环境中的组织。在这个项目中，我将与Joerg Bewersdorf(美国耶鲁大学纽黑文分校)领导的团队密切合作，他正在开发PanVision协议。AIM 1和AIM 2已经有了足够的数据，更多的图像也在处理中。计划与圣约翰斯顿实验室(剑桥大学)进行更多的合作，将这种成像方案应用于完整的组织。这项工作将增强非监督分割算法，使它们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对细胞超微结构进行自动分割和形状分析的成像管道，实现新的生物学发现，并增强对基本生物过程的理解。该项目属于EPSRC医疗保健技术/医学成像研究领域。

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.