Computational Diagnostics Lab, UCLA, Los Angeles, USA; The Department of Electrical and Computer Engineering, UCLA, Los Angeles, USA.
Computational Diagnostics Lab, UCLA, Los Angeles, USA; The Department of Bioengineering, UCLA, Los Angeles, USA.
Med Image Anal. 2022 Jan;75:102251. doi: 10.1016/j.media.2021.102251. Epub 2021 Nov 3.
Semantic segmentation of histopathology images can be a vital aspect of computer-aided diagnosis, and deep learning models have been effectively applied to this task with varying levels of success. However, their impact has been limited due to the small size of fully annotated datasets. Data augmentation is one avenue to address this limitation. Generative Adversarial Networks (GANs) have shown promise in this respect, but previous work has focused mostly on classification tasks applied to MR and CT images, both of which have lower resolution and scale than histopathology images. There is limited research that applies GANs as a data augmentation approach for large-scale image semantic segmentation, which requires high-quality image-mask pairs. In this work, we propose a multi-scale conditional GAN for high-resolution, large-scale histopathology image generation and segmentation. Our model consists of a pyramid of GAN structures, each responsible for generating and segmenting images at a different scale. Using semantic masks, the generative component of our model is able to synthesize histopathology images that are visually realistic. We demonstrate that these synthesized images along with their masks can be used to boost segmentation performance, especially in the semi-supervised scenario.
组织病理学图像的语义分割是计算机辅助诊断的一个重要方面，深度学习模型已经在该任务中得到了有效应用，取得了不同程度的成功。然而，由于完全标注数据集的规模较小，它们的影响受到了限制。数据增强是解决这一限制的一种途径。生成对抗网络（GAN）在这方面显示出了潜力，但之前的工作主要集中在应用于磁共振（MR）和计算机断层扫描（CT）图像的分类任务上，而这些图像的分辨率和规模都低于组织病理学图像。应用 GAN 作为大规模图像语义分割的数据增强方法的研究较少，这种方法需要高质量的图像-掩模对。在这项工作中，我们提出了一种用于高分辨率、大规模组织病理学图像生成和分割的多尺度条件 GAN。我们的模型由一个 GAN 结构的金字塔组成，每个结构负责在不同的尺度上生成和分割图像。通过语义掩模，我们的模型的生成部分能够合成具有逼真视觉效果的组织病理学图像。我们证明，这些合成图像及其掩模可以用于提高分割性能，特别是在半监督场景下。
