论文标题
使用标签到图像翻译进行核图像分割的生成合成增强
Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation
论文作者
论文摘要
在医学图像诊断中，使用语义分割的病理图像分析对于作为数字病理领域的有效筛查变得很重要。空间增强是普通的，用于语义分割。恶性下的肿瘤图像很少见，以注释核区域的标签需要耗时很多。我们需要有效利用数据集来最大化分割精度。预计会改变一般图像的一些扩展会影响分割性能。我们提出了使用标签到图像翻译的合成增强，从带有边缘结构的语义标签映射到真实图像。本文恰恰涉及肿瘤中核的染色玻片。实际上，我们演示了应用于最初数据集的几种分割算法，该算法包含使用合成增强的真实图像和标签，以添加其广义图像。我们计算并报告提出的合成增强程序提高了其准确性。
In medical image diagnosis, pathology image analysis using semantic segmentation becomes important for efficient screening as a field of digital pathology. The spatial augmentation is ordinary used for semantic segmentation. Tumor images under malignant are rare and to annotate the labels of nuclei region takes much time-consuming. We require an effective use of dataset to maximize the segmentation accuracy. It is expected that some augmentation to transform generalized images influence the segmentation performance. We propose a synthetic augmentation using label-to-image translation, mapping from a semantic label with the edge structure to a real image. Exactly this paper deal with stain slides of nuclei in tumor. Actually, we demonstrate several segmentation algorithms applied to the initial dataset that contains real images and labels using synthetic augmentation in order to add their generalized images. We computes and reports that a proposed synthetic augmentation procedure improve their accuracy.
