Unsupervised Nuclei Segmentation Using Spatial Organization Priors
Unsupervised Nuclei Segmentation Using Spatial Organization Priors
复制标题
使用空间组织先验的无监督细胞核分割
DOI：
10.1007/978-3-031-16434-7_32
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Hugues Talbot
中科院分区：
文献类型：
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作者：
Loïc Le Bescond;Marvin Lerousseau;I. Garberis;Fabrice André;S. Christodoulidis;M. Vakalopoulou;Hugues Talbot
在数字病理学中，病理学家通常通过免疫组织化学染色的载玻片对各种生物标志物（例如 KI67、HER2、CD3/CD8）进行分析。在患者活检中识别这些生物标志物可以更明智地设计他们的治疗方案。这些类型图像的多样性和特殊性使得注释数据库的可用性稀疏。因此，稳健且高效的基于学习的诊断系统很难在临床环境中开发和应用。我们的研究建立在观察组织的整体组织和结构在不同染色方案中相似的观察基础上。在本文中，我们建议利用苏木精-伊红染色数据库的广泛可用性和组织组织和结构的不变性，以便对免疫组织化学图像进行无监督的细胞核分割。我们通过与大量可用的苏木精-伊红染色细胞核掩模进行比较，实施并评估了一种依赖于高级细胞核分布先验的生成对抗方法。与经典的无监督和监督方法相比，我们的方法显示出有希望的结果，正如我们在两个公开可用的数据集上定量证明的那样。我们的代码是公开的，以鼓励进一步的贡献（https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors）。
In digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immuno-histo-chemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of haematoxylin-eosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available haematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we quantitatively demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions (https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors).