Faculty of Computer Science, Federal University of Uberlândia, Brazil and Institute of Biomedical Science, Federal University of Uberlândia, Uberlândia, Brazil.
J Digit Imaging. 2023 Aug;36(4):1608-1623. doi: 10.1007/s10278-023-00814-z. Epub 2023 Apr 3.
Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting problem when only a few training samples are available. This paper proposes a new data augmentation strategy, named Random Composition Augmentation (RCAug), to train fully convolutional networks (FCN) to segment OSCC tumor regions in H &E-stained histological images. Given the input image and their corresponding label, a pipeline with a random composition of geometric, distortion, color transfer, and generative image transformations is executed on the fly. Experimental evaluations were performed using an FCN-based method to segment OSCC regions through a set of different data augmentation transformations. By using RCAug, we improved the FCN-based segmentation method from 0.51 to 0.81 of intersection-over-union (IOU) in a whole slide image dataset and from 0.65 to 0.69 of IOU in a tissue microarray images dataset.
在诊断包括口腔鳞状细胞癌（OSCC）在内的不同类型癌症时，对病理学家来说，对 H&E 染色切片中的肿瘤区域进行分割是一项重要任务。由于对组织学图像进行标记是一项高度熟练、复杂且耗时的任务，因此，标记组织学图像的可用性通常会限制图像分割。因此，数据扩充策略对于训练卷积神经网络模型变得至关重要，以克服在只有少数训练样本可用时的过拟合问题。本文提出了一种新的数据扩充策略，称为随机组合扩充（RCAug），用于训练全卷积网络（FCN），以分割 H&E 染色组织学图像中的 OSCC 肿瘤区域。给定输入图像及其对应的标签，在输入图像上实时执行具有几何、失真、颜色转换和生成式图像变换的随机组合的流水线。通过使用基于 FCN 的方法，通过一组不同的数据扩充变换来分割 OSCC 区域，进行了实验评估。通过使用 RCAug，我们将基于 FCN 的分割方法的整体幻灯片图像数据集的交并比（IOU）从 0.51 提高到 0.81，组织微阵列图像数据集的 IOU 从 0.65 提高到 0.69。
