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尤其成功，这需要人类注释员进行多年的培训。这个叙述中的一个关键遗漏是，为了训练这样的模型，首先需要大量的手动注释。尽管机器视觉在过去几年中取得了突破性的创新，但人们的大部分注意力都集中在可以随时获得大量注释数据集的疾病或模式上。虽然图像级别的注释可以在合理的时间范围内实现，但按像素进行注释则很难按比例获得。分割是许多基于图像的诊断管道的常年组成部分，在这些管道中，解剖结构或异常的边界被勾画出来，以便于计算形态特征(例如，大小和形状)、监控生长/收缩，以及规划手术或治疗过程。一如既往，这个过程几乎没有出错的余地。在许多学科中，手动描绘医学图像仍然是黄金标准，需要专家注释员付出极大的努力和艰苦的努力。随着医疗保健领域对基于人工智能的解决方案的需求不断增加，人们已经转向了可以利用噪声标签的技术。弱监督学习是一种以不太完美的标签的形式提供基本事实数据的范式。这种不完美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轻注释负担的一种手段。对于医学图像分割，弱标签可能采取已经作为常规临床护理的一部分收集的数据的形式，但不能完全分割。例如，测量肿瘤的垂直直径(即主轴)通常是为了估计一段时间内的横截面面积，以监测治疗反应。技术，如RECIST(艾森豪威尔等人2009)和RANO(温氏等人与完整的分割相比，执行起来花费的时间要少得多，但仍然需要肿瘤表现和形态的全面知识。这推动了自动化方法的发展，这些方法可以学习如何执行此类测量，并将其知识转移到无需配对地面真实数据的情况下执行分割。本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，从医学图像中执行二维测量：RECIST(用于肺部肿瘤)和RANO(用于脑肿瘤)。基于DenseNet的主干网(Huang et al.2017)将用于低级特征学习，该学习连接到用于主轴和质心估计的专业层。然后，将专门化的层融合成最终的分割预测层。对抗性学习(古德费罗等人)2014)将用于促进产生视觉上与外部数据集中的标签地图相似的标签地图。原则上，这些数据可能来自不同的患者群体或成像模式。该方法将使用两个公开可用的数据集来开发和验证：BRATS(胶质瘤的多序列MR图像)和TCIA(肺部肿瘤的CT图像)。二维测量将使用以前的方法从标签映射数据合成(Chang等人。2019年)，引入变量以模拟在临床医生中观察到的评分者之间的可变性。将进行深入的评估，以确定实现具有竞争力的分割性能所需的弱监督数据的数量和质量。将向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.