Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA; Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53706, USA.
Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53706, USA.
Med Image Anal. 2023 Dec;90:102961. doi: 10.1016/j.media.2023.102961. Epub 2023 Sep 12.
The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.
在从癌症到慢性炎症等疾病情况下，纤维胶原在组织微环境中的作用至关重要，许多研究都证明了这一点。定量纤维胶原组织对于描述胶原纤维的拓扑结构以及研究胶原纤维在疾病进展中的作用是一种强有力的方法。我们提出了一种基于深度学习的方法，用于对病理组织样本的基于显微镜的胶原图像中的胶原纤维的拓扑性质进行定量。我们的方法利用深度神经网络提取胶原纤维中心线，并利用深度生成模型创建合成训练数据，以解决当前大规模注释的不足。作为这项工作的一部分，我们创建并注释了一个胶原纤维中心线数据集，希望能促进该领域的进一步研究。基于中心线提取结果，可以得出纤维方向、排列、密度和长度等定量测量结果。我们的流水线包含三个阶段。首先，训练变分自编码器生成具有可控拓扑性质的合成中心线。然后，条件生成对抗网络从合成中心线合成真实的胶原纤维图像，从而生成图像-中心线对的合成训练集。最后，我们使用原始数据和合成数据训练胶原纤维中心线提取网络。使用二次谐波显微镜采集的胰腺、肝脏和乳腺癌样本的胶原纤维图像进行评估，表明我们的流水线优于几种流行的纤维中心线提取工具。将合成数据纳入训练进一步提高了网络的泛化能力。我们的代码可在 https://github.com/uw-loci/collagen-fiber-metrics 上获取。
