STUDY QUESTION How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition? SUMMARY ANSWER A combination of a machine learning-based tissue analysis software for tissue segmentation and a deep learning-based algorithm for segmentation-independent cell identification shows strong performance on the automated histological analysis of endometriosis sections. WHAT IS KNOWN ALREADY Endometriosis is characterized by the complex interplay of various cell types and exhibits great variation between patients and endometriosis subtypes. STUDY DESIGN, SIZE, DURATION Endometriosis tissue samples of eight patients of different subtypes were obtained during surgery. PARTICIPANTS/MATERIALS, SETTING, METHODS Endometriosis tissue was formalin-fixed and paraffin-embedded before sectioning and staining by (multiplex) immunohistochemistry. A 6-plex immunofluorescence panel in combination with a nuclear stain was established following a standardized protocol. This panel enabled the distinction of different tissue structures and dividing cells. Artificial intelligence-based tissue and cell phenotyping were employed to automatically segment the various tissue structures and extract quantitative features. MAIN RESULTS AND THE ROLE OF CHANCE An endometriosis-specific multiplex panel comprised of PanCK, CD10, α-SMA, calretinin, CD45, Ki67, and DAPI enabled the distinction of tissue structures in endometriosis. Whereas a machine learning approach enabled a reliable segmentation of tissue substructure, for cell identification, the segmentation-free deep learning-based algorithm was superior. LIMITATIONS, REASONS FOR CAUTION The present analysis was conducted on a limited number of samples for method establishment. For further refinement, quantification of collagen-rich cell-free areas should be included which could further enhance the assessment of the extent of fibrotic changes. Moreover, the method should be applied to a larger number of samples to delineate subtype-specific differences. WIDER IMPLICATIONS OF THE FINDINGS We demonstrate the great potential of combining multiplex staining and cell phenotyping for endometriosis research. The optimization procedure of the multiplex panel was transferred from a cancer-related project, demonstrating the robustness of the procedure beyond the cancer context. This panel can be employed for larger batch analyses. Furthermore, we demonstrate that the deep learning-based approach is capable of performing cell phenotyping on tissue types that were not part of the training set underlining the potential of the method for heterogenous endometriosis samples. STUDY FUNDING/COMPETING INTEREST(S) All funding was provided through departmental funds. The authors declare no competing interests. TRIAL REGISTRATION NUMBER N/A.

中文翻译：

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多重染色组织学子宫内膜异位症切片中基于人工智能的组织分割和细胞鉴定


研究问题 我们如何最好地实现多通道染色子宫内膜异位症切片的组织分割和细胞计数以了解组织组成？总结答案：用于组织分割的基于机器学习的组织分析软件和用于不依赖分割的细胞识别的基于深度学习的算法相结合，在子宫内膜异位症切片的自动组织学分析中显示出强大的性能。已知的子宫内膜异位症的特征是各种细胞类型之间的复杂相互作用，并且在患者和子宫内膜异位症亚型之间表现出很大差异。手术期间获得 8 名不同亚型患者的子宫内膜异位症组织样本的研究设计、规模、持续时间。参与者/材料、设置、方法子宫内膜异位症组织在切片和（多重）免疫组织化学染色之前用福尔马林固定和石蜡包埋。按照标准化方案建立 6 重免疫荧光面板与核染色的组合。该检测组合能够区分不同的组织结构和分裂的细胞。采用基于人工智能的组织和细胞表型分析来自动分割各种组织结构并提取定量特征。主要结果和机会的作用由 PanCK、CD10、α-SMA、钙调蛋白、CD45、Ki67 和 DAPI 组成的子宫内膜异位症特异性多重检测组能够区分子宫内膜异位症的组织结构。虽然机器学习方法可以可靠地分割组织亚结构，但对于细胞识别，基于无分割的深度学习算法更胜一筹。局限性，谨慎原因 本分析是对有限数量的样品进行的，以便建立方法。 为了进一步细化，应包括富含胶原蛋白的无细胞区域的量化，这可以进一步加强对纤维化变化程度的评估。此外，该方法应应用于更多的样本，以描绘特定于亚型的差异。研究结果的更广泛意义 我们展示了将多重染色和细胞表型相结合用于子宫内膜异位症研究的巨大潜力。多重面板的优化程序是从癌症相关项目转移过来的，证明了该程序在癌症背景之外的稳健性。该面板可用于大批量分析。此外，我们证明基于深度学习的方法能够对不属于训练集的组织类型进行细胞表型分析，这强调了该方法对异质性子宫内膜异位症样本的潜力。研究资金/竞争利益 所有资金均由部门资金提供。作者声明没有利益冲突。试验注册号 N/A。