SynCLay: Interactive synthesis of histology images from bespoke cellular layouts.
SynCLay: Interactive synthesis of histology images from bespoke cellular layouts.
复制标题
SynCLay：根据定制的细胞布局交互式合成组织学图像。
DOI：
10.1016/j.media.2023.102995
复制
发表时间：
2023
影响因子：
10.9
通讯作者：
Deshpande S
中科院分区：
文献类型：
--
作者：
Deshpande S
组织学图像的自动合成在计算病理学中有几个潜在的应用。然而，没有现有的方法可以生成具有定制细胞布局或用户定义的组织学参数的逼真组织图像。在这项工作中，我们提出了一个名为SynCLay （Synthesis from Cellular Layouts）的新框架，它可以从用户定义的细胞布局以及带注释的细胞边界构建逼真的高质量组织学图像。通过提出的框架，基于定制细胞布局的组织图像生成允许用户从不同类型细胞（例如，中性粒细胞、淋巴细胞、上皮细胞等）的任意拓扑排列中生成不同的组织学模式。SynCLay生成的合成图像有助于研究不同类型的细胞在肿瘤微环境中的作用。此外，它们可以通过最小化数据不平衡的影响来帮助平衡组织图像中细胞计数的分布，从而设计准确的细胞组成预测器。我们以对抗的方式训练SynCLay，并在其训练中集成核分割和分类模型，以细化核结构并结合合成图像生成核掩模。在推理过程中，我们将该模型与另一个参数模型结合起来，生成冒号图像和相关的细胞计数，作为给定不同细胞的分化等级和细胞性（细胞密度）的注释。我们使用Frechet Inception Distance定量评估生成的图像，并报告来自训练有素的病理学家的反馈，他们将真实感评分分配给框架生成的一组图像。所有病理学家对合成图像的平均真实感得分与对真实图像的得分一样高。此外，在病理学家的帮助下，我们展示了生成的图像准确区分良性和恶性肿瘤的能力，从而增强了它们的可靠性。我们证明了所提出的框架可以用于向组织图像中添加新细胞并改变细胞位置。我们还表明，用我们的框架生成的合成数据增强有限的真实数据可以显著提高细胞成分预测任务的预测性能。所建议的SynCLay框架的实现可以在https://github.com/Srijay/SynCLay-Framework上获得。
Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular Layouts) that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries. Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells (e.g., neutrophils, lymphocytes, epithelial cells and others). SynCLay generated synthetic images can be helpful in studying the role of different types of cells present in the tumor microenvironment. Additionally, they can assist in balancing the distribution of cellular counts in tissue images for designing accurate cellular composition predictors by minimizing the effects of data imbalance. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model in its training to refine nuclear structures and generate nuclear masks in conjunction with synthetic images. During inference, we combine the model with another parametric model for generating colon images and associated cellular counts as annotations given the grade of differentiation and cellularities (cell densities) of different cells. We assess the generated images quantitatively using the Frechet Inception Distance and report on feedback from trained pathologists who assigned realism scores to a set of images generated by the framework. The average realism score across all pathologists for synthetic images was as high as that for the real images. Moreover, with the assistance from pathologists, we showcase the ability of the generated images to accurately differentiate between benign and malignant tumors, thus reinforcing their reliability. We demonstrate that the proposed framework can be used to add new cells to a tissue images and alter cellular positions. We also show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task. The implementation of the proposed SynCLay framework is available at https://github.com/Srijay/SynCLay-Framework.