Solid tumour segmentation via principal axis estimation using weakly supervised adversarial deep learning

近年来，深度学习在医学成像文献中占据主导地位。卷积神经网络（CNN）在学习在几个小时内执行高度复杂的任务方面特别成功，这些任务需要人类注释者进行多年的训练。这种叙述中的一个关键遗漏是首先需要大量的手动注释来训练这样的模型。尽管在过去的几年里机器视觉有了突破性的创新，但大部分注意力都集中在疾病或易于获得大型注释数据集的模式上。虽然图像级注释可以在合理的时间范围内实现，但按像素的注释要大规模获得要困难得多。分割是许多基于图像的诊断流程的长期组成部分，其中解剖结构或异常的边界被描绘以使得能够计算形态特征（例如，大小和形状）、监测生长/收缩以及规划手术或治疗程序。当然，在这个过程中几乎没有出错的余地。医学图像的手动描绘仍然是许多学科中的黄金标准，需要专家注释者的高度费力和艰苦的努力。鉴于医疗保健领域对基于人工智能的解决方案的需求日益增长，人们已经转向可以利用噪声标签的技术。弱监督学习是一种范式，其中地面真实数据以不太完美的标签形式提供。这种缺陷通常是由于注释过程中的固有噪声造成的，但越来越多地被设计为减少注释负担的手段。对于医学图像分割，弱标签可以采取已经作为常规临床护理的一部分收集的数据的形式，但是达不到完整的分割。例如，通常进行肿瘤垂直直径（即主轴）的测量以估计随时间推移的横截面积，从而监测治疗反应。RECIST（Eisenhower et al. 2009）和RANO（Wen et al. 2010）等技术比完整分割耗时少得多，但仍需要全面了解肿瘤表现和形态。这激发了自动化方法的开发，这些方法可以学习如何执行这种测量并将其知识转移到执行分割而无需配对的地面实况数据。这个博士项目旨在开发一种基于弱监督和对抗性深度学习的医学图像分割方法。CNN将接受训练，以执行医学图像的二维测量：RECIST（用于肺肿瘤）和RANO（用于脑肿瘤）。基于DenseNet（Huang et al. 2017）的骨干网络将用于低级特征学习，该网络连接到主轴和质心估计的专用层。然后，将专门的层融合到最终的分割预测层中。对抗性学习（Goodfellow et al. 2014）将用于促进标签映射的生成，这些标签映射在视觉上与来自外部数据集的标签映射相似。原则上，这些数据可能来自不同的患者人群或成像模式。该方法将使用两个公开的数据集进行开发和验证; BraTS（神经胶质瘤的多序列MR图像）和TCIA（肺肿瘤的CT图像）。将使用先前的方法（Chang等人，2019）从标签图数据中合成生成二维测量值，引入变化以模拟临床医生中观察到的评估者间变异性。将进行深入评估，以确定实现竞争性分割性能所需的弱监督数据的数量和质量。将从林肯县医院寻求临床支持和验证数据。

Deep learning has dominated the medical imaging literature in recent years. Convolutional neural networks (CNNs) have been particularly successful at learning to perform highly complex tasks in a matter of hours that require many years of training on the part of a human annotator. A critical omission from this narrative is the volume of manual annotation required in the first place in order to train such models. Despite ground- breaking innovations in machine vision over the past few years, much of the attention has been focused on diseases or modalities for which large annotated datasets are readily available. While image-level annotation may be achieved in a reasonable timeframe, per-pixel annotations are considerably harder to obtain at scale. Segmentation is a perennial component of many image-based diagnosis pipelines, where the boundaries of anatomical structures or anomalies are delineated to enable calculation of morphological features (e.g. size & shape), monitoring of growth/shrinkage, and planning for surgical or therapeutic procedures. Invariably, there is little room for error in this process. Manual delineation of medical images remains the gold standard in many disciplines, requiring highly laborious and painstaking efforts on the part of expert annotators. In light of increasing demands for AI-based solutions in healthcare, there has been a shift towards techniques that can leverage noisy labels.Weakly supervised learning is a paradigm where ground truth data are provided in the form of less-than-perfect labels. This imperfection can often be due to inherent noise in the annotation process, but is increasingly by design as a means of reducing the annotation burden. For medical image segmentation, weak labels may take the form of data that are already collected as part of routine clinical care but fall short of a complete segmentation. For example, the measurement of a tumour's perpendicular diameters (i.e. principal axes) is often performed to estimate cross-sectional area over time to monitor treatment response. Techniques such as RECIST (Eisenhower et al. 2009) and RANO (Wen et al. 2010) are considerably less time consuming to perform than a complete segmentation, but still require comprehensive knowledge of tumour presentation and morphology. This motivates the development of automated methods that can learn how to perform such measurements and transfer their knowledge to perform segmentation without paired ground truth data. This PhD project aims to develop a medical image segmentation approach based on weakly supervised and adversarial deep learning. CNNs will be trained to perform bidimensional measurements from medical images: RECIST (for lung tumours) and RANO (for brain tumours). A backbone network based on DenseNet (Huang et al. 2017) will be used for low-level feature learning, which is connected to specialised layers for principal axis and centroid estimation. The specialised layers will then be fused into a final segmentation prediction layer. Adversarial learning (Goodfellow et al. 2014) will be used to promote the generation of labelmaps that appear visually similar to those from an external dataset. In principle, these data may have originated from a different patient population or imaging modality. The method will be developed and validated using two publicly available datasets; BraTS (multi-sequence MR images of glioma) and TCIA (CT images of lung tumours). Bidimensional measurements will be synthetically generated from labelmap data using previous methods (Chang et al. 2019), with variations introduced to mimic the inter-rater variability observed among clinicians. An in-depth evaluation will be conducted to determine the quantity and quality of weakly supervised data needed to achieve competitive segmentation performance. Clinical support and data for validation will be sought from Lincoln County hospital.