Synergistic Representation Learning for Pancreatic Image Analysis

博士项目目标：在这个项目中，我们的目标是开发新的机器学习方法来分割和分析胰腺图像。该项目将实现胰腺体积和形状的稳健和准确表征，为胰腺解剖评估和病理特征识别提供定量成像表型。项目描述：胰腺癌是英国第六大最常见的癌症死亡原因，5年生存率仅为5%。然而，如果胰腺癌在早期阶段被诊断出来，手术是可能的，存活率可以达到20%。胰腺癌的早期诊断具有挑战性，主要是因为症状只在晚期出现，而且仍然缺乏筛查工具。在这个项目中，我们研究了新的机器学习方法，用于从医学图像中自动分割和分析胰腺解剖结构。它将为提取定量的基于图像的生物标志物提供有效的工具，并协助临床医生诊断和评估胰腺疾病。近年来，人们提出了许多胰腺图像分割方法。有些是基于图集的，依靠图像配准进行图集传播，然后进行标签融合来创建分割[3]。基于图集的方法的缺点是，由于多图像配准的成本，它们的计算成本很高。最近的方法是基于深度学习的，训练卷积神经网络学习从图像到分割[4]-[10]的映射。由于使用gpu和单次推理过程，它们的计算速度更快。最先进的分割方法可以实现正常胰腺[4]的平均Dice重叠度为86.9%。然而，对于异常胰腺，Dice指标可低至38.4%[4]。这表明了胰腺图像分割的技术挑战。这些挑战可归因于几个因素。首先，与其他腹部器官相比，胰腺很小，只占3D视野的一小部分。由于类不平衡问题，神经网络对小对象的敏感性较低。其次，胰腺在解剖形状和外观上变化很大。它的解剖结构会随着年龄的增长而改变，从而导致萎缩、分叶化和脂肪变性。对于病理病例，解剖结构也可能受到囊肿和肿瘤的显著影响。第三，神经网络的训练需要大量的数据集。具有手动注释的可用训练数据在临床场景中通常是有限的。为了解决这些挑战，我们提出了一种用于胰腺图像分割的协同表示学习方法，以提高鲁棒性和准确性。这种协同效应将来自多个方面。1)尺度间协同：多尺度语义信息将以一种联合的、从粗到精的方式被整合。2)图像特征与解剖先验的协同：学习解剖形状先验，提高分割的鲁棒性。3)数据之间的协同：全标记（多器官注释），部分标记（仅胰腺注释）和未注释的数据将用于半监督和部分监督学习。4)模式之间的协同作用：将探索CT和MR模式用于语义特征学习。5)人机协同。检测异常案例和难例，供人工审阅和注释，实现人在循环学习。该项目的输出将是一个自动化工具，可应用于胰腺成像表型分析的大规模数据集。期望候选人的背景是工程，计算机或物理科学。

Aim of the PhD Project:In this project, we aim to develop novel machine learning approaches for segmentation and analysis of pancreatic images.The project will enable robust and accurate characterisation of pancreatic volumes and shapes, providing quantitative imaging phenotypes for assessment of pancreatic anatomy and identification of pathological features.Project Description:Pancreatic cancer is the 6th most common cause of cancer deaths in the UK with a 5-year survival rate of only 5% [1]. However, if pancreatic cancer is diagnosed at an early stage when surgery is possible, the survival rate can go up to 20% [1], [2]. Early diagnosis of pancreatic cancer is challenging, mainly because symptoms only occur at a late stage and screening tools are still lacking. In this project, we investigate novel machine learning approaches for automated segmentation and analysis of pancreatic anatomy from medical images. It will provide an efficient tool for extracting quantitative image-based biomarkers and assisting clinicians in diagnosis and assessment of pancreatic diseases.A number of methods have been proposed for pancreatic image segmentation in recent years. Some are atlas-based, relying on image registration for atlas propagation and then performing label fusion to create segmentation [3]. A disadvantage with atlas-based methods is that they are computationally expensive due to the cost of multiple image registrations. Most recent methods are deep learning-based, which train convolutional neural networks to learn the mapping from image to segmentation [4]-[10]. They are computationally faster due to the use of GPUs and the one-pass inference process.State-of-the-art segmentation methods can achieve an average Dice overlap metric of 86.9% for normal pancreas [4]. However, for abnormal pancreas, the Dice metric can be as low as 38.4% [4]. This demonstrates the technical challenges in pancreatic image segmentation. The challenges are attributed to several factors. First, the pancreas is small compared to other abdominal organs, occupying only a small proportion of the 3D field-of-view. Neural networks are less sensitive to small objects due to the class imbalance problem. Second, the pancreas is highly variable in anatomical shape and appearance. Its anatomy is altered by ageing, which causes atrophy, lobulation and fatty degeneration. For pathological cases, the anatomy can also be significantly influenced by cysts and tumours. Third, the training of neural networks requires large datasets. Available training data with manual annotations are often limited in clinical scenarios.To address these challenges, we propose a synergistic representation learning approach for pancreatic image segmentation to improve both the robustness and accuracy. The synergy will come from multiple aspects. 1) Synergy between scales: Multi-scale semantic information will be incorporated in a joint and coarse-to-fine fashion. 2) Synergy between image features and anatomical priors: Anatomical shape priors will be learnt to improve segmentation robustness. 3) Synergy between data: Fully-labelled (multi-organ annotation), partially-labelled (pancreas-only annotation) and unannotated data will be utilised for semi- and partially-supervised learning. 4) Synergy between modalities: Both CT and MR modalities will be explored for semantic feature learning. 5) Synergy between computer and human. Abnormal cases and hard examples will be detected for human to review and annotate to enable human-in-the-loop learning.The output of the project will be an automated tool that can be applied to large-scale datasets for analysis of pancreatic imaging phenotypes. The expected candidate's background is engineering, computing or physical sciences.