Generating realistic tissue images with annotations is a challenging task that is important in many computational histopathology applications. Synthetically generated images and annotations are valuable for training and evaluating algorithms in this domain. To address this, we propose an interactive framework generating pairs of realistic colorectal cancer histology images with corresponding glandular masks from glandular structure layouts. The framework accurately captures vital features like stroma, goblet cells, and glandular lumen. Users can control gland appearance by adjusting parameters such as the number of glands, their locations, and sizes. The generated images exhibit good Frechet Inception Distance (FID) scores compared to the state-of-the-art image-to-image translation model. Additionally, we demonstrate the utility of our synthetic annotations for evaluating gland segmentation algorithms. Furthermore, we present a methodology for constructing glandular masks using advanced deep generative models, such as latent diffusion models. These masks enable tissue image generation through a residual encoder-decoder network.

翻译：生成带有标注的真实组织图像是一项具有挑战性的任务，在计算病理学的众多应用中至关重要。合成生成的图像和标注对于该领域算法的训练与评估具有重要价值。为此，我们提出一个交互式框架，能够从腺体结构布局生成成对的真实结直肠癌组织学图像及其对应腺体掩模。该框架可精确捕获基质、杯状细胞及腺腔等关键特征。用户可通过调整腺体数量、位置及大小等参数控制腺体外观。与当前最先进的图像到图像翻译模型相比，生成的图像在弗雷歇初始距离（FID）评分上表现优异。此外，我们展示了合成标注在评估腺体分割算法中的实用性。最后，我们提出一种利用先进深度生成模型（如潜在扩散模型）构建腺体掩模的方法，通过残差编码器-解码器网络实现组织图像的生成。