Unsupervised Annotation of Complex 3D BioMedical Data.

复杂 3D 生物医学数据的无监督注释。

• 批准号: 2882348
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882348
• 关键词: Unsupervised; Annotation; Complex; 3D; BioMedical

We aim to develop new methods for the unsupervised annotation of complex 3D biomedical data. This will allow for the efficient labelling of 3D models of cellular data for use by either human biomedical researchers or future machine learning models. Deep learning methods are increasingly being used to analyse complex data however these methods require large quantities of labelled training data to be effective. Such training data is often difficult and expensive to acquire. The recent availability of large volumes of 3D data poses challenges in its annotation as this is a significantly more difficult task than the 2D equivalent. This presents a barrier to research. The solution we propose is using unsupervised annotation. While labelling data from scratch is prohibitively expensive, requiring a significant time investment from a team of experts, combining and manipulating labels obtained via unsupervised methods provides a more realistic approach. Through this method we can reduce the time and monetary investment required to produce labelled training data for further research and provide significant utility for future studies.Furthermore, Virtual Reality (VR) tools have been developed in recent years which offer an immersive view of 3D cellular models allowing doctors and researchers intuitive, detailed views of cells they wish to analyse. Of particular interest is the is the MiCellAnnGELo cell painting tool which offers manual annotation of 3D biomedical data while working with 4D timeseries data. The immersive nature of VR is well suited to this task as it can allow experts a close-up interactive view of cells to streamline the annotation process. MiCellAnnGELo makes use of the Unity gaming engine, a platform designed for the navigation of 3D environments, to allow for cell models to be easily manipulated by the end user. This provides an easy-to-use platform for the annotation of biomedical data that can alleviate much of the prohibitive nature of 3D annotation.We will develop new functionality to facilitate the dynamic labelling of cell components. As fully automating the annotation process may jeopardise accuracy, we aim to incorporate suggestions into the tool to identify likely cell structures and components within a model. In doing so we can streamline the annotation process further and increase the utility of these visualisation tools, allowing researchers to better analyse complex structures within cells while providing a visual aid to facilitate a better understanding of the cellular data.We will make use of standard frameworks and models to achieve our goals. For the development of our neural networks, we intend to use the PyTorch framework due to its ease of use and flexibility which will allow us to quickly prototype and iterate on our research. Should problems arise with PyTorch, the TensorFlow and Keras frameworks are available as alternatives. For our models, we plan to base our work upon Graph convolutional Networks for surface data and 3D Convolutional Neural Networks for volume data. Cluster based Generative Adversarial Networks will be used to simulate additional training data. Currently clustering has been primarily applied to 2D datasets where the number of expected clusters is already known. An important component of our project is therefor to develop strategies for combining networks which are either based on different numbers of clusters or using clustering methods that do not depend on a fixed number. An important contribution will then be to implement the clustering of data at different resolutions using subsampling that can preserve the topology of surface meshes, and superpixel approaches for volume data. We must ensure that our model can train on and annotate data of different resolutions and is not limited to a single size of input. By adapting and combining these models we achieve a strong foundation for our research and obtain a basis for the segmentation and annotation of our own 3Ddata

我们的目标是开发新的方法来对复杂的3D生物医学数据进行无监督标注。这将允许对细胞数据的3D模型进行有效的标记，以供人类生物医学研究人员或未来的机器学习模型使用。深度学习方法越来越多地被用于分析复杂的数据，然而这些方法需要大量标记的训练数据才能有效。这样的训练数据通常很难获得，而且成本很高。最近获得的大量3D数据对其注释提出了挑战，因为这是一项比2D等效物困难得多的任务。这给研究带来了障碍。我们提出的解决方案是使用无监督标注。虽然从头开始标记数据的成本高得令人望而却步，需要专家团队投入大量时间，但组合和处理通过非监督方法获得的标记提供了一种更现实的方法。通过这种方法，我们可以减少为进一步研究产生标记训练数据所需的时间和金钱投资，并为未来的研究提供重要的实用工具。此外，近年来开发的虚拟现实(VR)工具提供了3D细胞模型的身临其境的视图，使医生和研究人员能够直观、详细地查看他们希望分析的细胞。特别令人感兴趣的是MiCellAnnGELo细胞绘制工具，它在处理4D时间序列数据时提供3D生物医学数据的手动注释。VR的身临其境的特性非常适合这项任务，因为它可以让专家近距离互动地查看单元格，以简化注释过程。MiCellAnnGELo利用Unity游戏引擎，这是一个为3D环境导航而设计的平台，允许最终用户轻松操作细胞模型。这为生物医学数据的注释提供了一个易于使用的平台，可以在很大程度上缓解3D注释的禁止性。我们将开发新的功能，以促进细胞组件的动态标记。由于完全自动化的注释过程可能会危及准确性，我们的目标是将建议整合到工具中，以识别模型中可能的细胞结构和组件。通过这样做，我们可以进一步简化注释过程，并增加这些可视化工具的实用性，使研究人员能够更好地分析细胞内的复杂结构，同时提供可视化辅助工具，以帮助更好地理解细胞数据。我们将利用标准框架和模型来实现我们的目标。对于我们的神经网络的开发，我们打算使用PyTorch框架，因为它易于使用和灵活，这将使我们能够快速制作原型并迭代我们的研究。如果PyTorch出现问题，可以使用TensorFlow和Kera框架作为替代。对于我们的模型，我们计划将我们的工作建立在曲面数据的图形卷积网络和体数据的3D卷积神经网络的基础上。基于簇的生成性对抗网络将被用来模拟额外的训练数据。目前，聚类主要应用于预期聚类数已知的2D数据集。因此，我们项目的一个重要组成部分是开发用于组合网络的策略，这些网络要么基于不同数量的集群，要么使用不依赖于固定数量的集群方法。然后，一个重要的贡献将是使用可以保留曲面网格拓扑的子采样和体数据的超像素方法来实现不同分辨率的数据的聚类。我们必须确保我们的模型能够训练和注释不同分辨率的数据，并且不限于单一大小的输入。通过对这些模型的改编和组合，为我们的研究奠定了坚实的基础，为我们自己的三维数据的分割和标注提供了基础