Health Data Science CDT

该项目旨在开发新的计算方法，用于分析胎盘组织学图像，以更好地了解孕产妇健康结果。胎盘在胎儿发育中起着至关重要的作用，可以为胎儿和产妇的健康提供有价值的见解。然而，胎盘分析仍然具有挑战性，由于器官的空间和时间的复杂性和异质性。该研究将利用深度学习和计算机视觉技术，从胎盘组织的全切片图像（WSI）中提取有生物学意义的信息。具体来说，该项目将延长现有的监督三级管道，（核-细胞-组织）到4阶段自监督学习管道（核-细胞-组织-病理学）来生成胎盘组织学的低维表示，其捕获特征而不依赖于专家注释。这种方法具有揭示通过传统分析方法可能不明显的模式和表型的潜力，并且将允许胎盘组织的高通量分析。在下游，这可以用于了解胎盘病理学和了解胎盘变异如何影响孕产妇健康。为胎盘组织学图像开发一个4阶段的自监督表示学习管道，将现有的关于WSI中细胞核、细胞和组织分类的工作扩展到包括病理识别，从而能够表征胎盘组织的正常变化和病理变化。开发基于图形的表示学习方法，以压缩和解释WSI数据，从而使下游分析能够对胎盘表示进行聚类。研究胎盘组织学表现与预测产妇健康结局的临床相关性，采用纵向队列数据。本计画将发展自我监督学习技术于胎盘病理学之研究领域.通过避免依赖专家注释，开发的方法有可能揭示无偏见的生物模式。该项目引入了一个多阶段可解释的管道，其中表征胎盘的每个阶段（核细胞组织病理学）都可以独立理解和分析，并可能应用于基础组织学模型。该项目将开发一种新的图形自动编码器架构，旨在压缩WSI信息，以实现高效的下游分析，同时保留结构信息。总体而言，该项目具有通过自动化胎盘分析功能提取生物学意义信息的重大影响潜力。这可能会导致更好地了解胎盘病理及其与孕产妇健康和产后风险的关系。这个项目福尔斯属于EPSRC医疗保健技术的主题，特别是“图像和视觉计算”和“生物信息学”的研究领域。

This project aims to develop novel computational methods for analysing placental histology images to better understand maternal health outcomes. The placenta plays a crucial role in fetal development and can provide valuable insights into both fetal and maternal health. However, placental analysis remains challenging due to the organ's spatial and temporal complexity and heterogeneity. The research will leverage deep learning and computer vision techniques to extract biologically meaningful information from whole slide images (WSIs) of placental tissue.Specifically, the project will extend an existing supervised 3-stage pipeline (nuclei-cell-tissue) to a 4-stage self-supervised learning pipeline (nuclei-cell-tissue-pathology) to generate low- dimensional representations of placental histology that capture features without relying on expert annotations. This approach has the potential to reveal patterns and phenotypes that may not be apparent through traditional analysis methods and will allow for high- throughput analysis of placental tissue. Downstream this can be used to characterise placenta pathologies and understand how placenta variation affects maternal health.The key objectives of the project are:1. Develop a 4-stage self-supervised representation learning pipeline for placentalhistology images, extending existing work on nuclei, cell, and tissue classification in WSIs to include pathology identification, enabling characterisation of normal variation and pathological changes of placental tissue.2. Develop methods for graph-based representation learning to compress and interpret WSI data to enable downstream analysis to cluster placentas representations.3. Investigate the clinical relevance of placental histology representations for predicting maternal health outcomes using longitudinal cohort data.The novelty of the research methodology is in several aspects. This project will develop self- supervised learning techniques to the understudied domain of placental pathology. By avoiding reliance on expert annotations, the methods developed have the potential to reveal unbiased biological patterns. The project introduces a multistage interpretable pipeline, where each stage of characterising the placenta (nuclei-cell-tissue-pathology) can be understood and analysed independently, with potential applications for a foundation histology model. The project will develop a new graph autoencoder architecture, designed to compress the WSI information to enable efficient downstream analysis while preserving structural information.Overall, the project has the potential for significant impact by automating placental analysis capabilities to extract biologically meaningful information. This could lead to improved understanding of placental pathologies and their relationship to maternal health and post- partum risks.This project falls within the EPSRC Healthcare Technologies theme, specifically the 'Image and Vision Computing' and 'Biological Informatics' research areas.