Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
Med Image Anal. 2019 Feb;52:199-211. doi: 10.1016/j.media.2018.12.001. Epub 2018 Dec 20.
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.
对结肠组织病理学图像中的腺体形态进行分析是确定结肠癌分级的重要步骤。尽管这项任务很重要，但手动分割既费力、耗时，又容易受到病理学家主观因素的影响。计算病理学的兴起导致了用于腺体分割的自动化方法的发展，这些方法旨在克服手动分割的挑战。然而，由于腺体外观的巨大可变性以及区分某些腺体和非腺体组织结构的困难，这项任务并不简单。此外，不确定性的度量对于诊断决策至关重要。为了解决这些挑战，我们提出了一种全卷积神经网络，通过在网络内的多个点重新引入原始图像，来克服最大池化导致的信息丢失。我们还使用带有不同扩张率的空洞空间金字塔池化来保留分辨率和多层次聚合。为了引入不确定性，我们在测试时引入随机变换，以获得同时生成不确定性图的增强分割结果，突出模糊区域。我们表明，该图可用于定义忽略具有高不确定性的预测的度量标准。所提出的网络在 GlA 挑战数据集和第二个独立的结直肠腺癌数据集上实现了最先进的性能。此外，我们在来自另外两个数据集的全幻灯片图像上进行腺体实例分割，以突出我们方法的通用性。作为扩展，我们引入了 MILD-Net 用于同时进行腺体和管腔分割，以提高网络的诊断能力。
