Unsupervised Annotation of Complex 3D BioMedical Data.

我们的目标是开发新的方法，复杂的三维生物医学数据的无监督注释。这将允许高效标记细胞数据的3D模型，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用于分析复杂数据，但这些方法需要大量标记的训练数据才能有效。此类训练数据的获取通常很困难且昂贵。最近大量3D数据的可用性对其注释提出了挑战，因为这是一项比2D等效物更困难的任务。这是研究的一个障碍。我们提出的解决方案是使用无监督注释。虽然从头开始标记数据非常昂贵，需要专家团队投入大量时间，但组合和操纵通过无监督方法获得的标签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生成标记训练数据所需的时间和资金投入，并为未来的研究提供重要的实用性。此外，近年来开发的虚拟现实（VR）工具提供了3D细胞模型的沉浸式视图，使医生和研究人员能够直观地详细了解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，该工具在处理4D时间序列数据时提供3D生物医学数据的手动注释。VR的沉浸式特性非常适合这项任务，因为它可以让专家近距离交互查看细胞，以简化注释过程。MiCellAnnGELo使用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纵细胞模型。这为生物医学数据的注释提供了一个易于使用的平台，可以减轻3D注释的限制性。我们将开发新的功能，以促进细胞成分的动态标记。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纳入工具中，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，并提高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，同时提供视觉辅助，以促进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，因为它的易用性和灵活性，这将使我们能够快速原型化和验证我们的研究。如果PyTorch出现问题，TensorFlow和Keras框架可以作为替代方案。对于我们的模型，我们计划将我们的工作基于用于表面数据的图卷积网络和用于体积数据的3D卷积神经网络。基于集群的生成对抗网络将用于模拟额外的训练数据。目前，聚类主要应用于2D数据集，其中预期聚类的数量是已知的。因此，我们的项目的一个重要组成部分是制定战略，结合网络，这是基于不同数量的集群或使用聚类方法，不依赖于一个固定的数字。然后，一个重要的贡献将是使用子采样来实现不同分辨率的数据聚类，该子采样可以保留表面网格的拓扑结构，以及用于体积数据的超像素方法。我们必须确保我们的模型可以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。通过调整和组合这些模型，我们为我们的研究奠定了坚实的基础，并为我们自己的3D数据的分割和注释提供了基础

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