Korman Scott E, Vissers Guus, Gorris Mark A J, Verrijp Kiek, Verdurmen Wouter P R, Simons Michiel, Taurin Sebastien, Sater Mai, Nap Annemiek W, Brock Roland
Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
Department of Obstetrics and Gynaecology, Radboudumc, Nijmegen, The Netherlands.
Hum Reprod. 2025 Mar 1;40(3):450-460. doi: 10.1093/humrep/deae267.
How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
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.
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.
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.
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.
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我们如何才能最好地实现对多通道染色的子宫内膜异位症切片进行组织分割和细胞计数，以了解组织组成？
一种基于机器学习的用于组织分割的组织分析软件与一种基于深度学习的用于独立于分割的细胞识别的算法相结合，在子宫内膜异位症切片的自动组织学分析中表现出强大的性能。
子宫内膜异位症的特征是多种细胞类型之间复杂的相互作用，并且在患者和子宫内膜异位症亚型之间表现出很大差异。
研究设计、规模、持续时间：在手术过程中获取了八名不同亚型患者的子宫内膜异位症组织样本。
参与者/材料、设置、方法：子宫内膜异位症组织经福尔马林固定、石蜡包埋，然后进行（多重）免疫组织化学切片和染色。按照标准化方案建立了一个包含6种荧光免疫标记物组合并结合核染色的检测方法。该检测方法能够区分不同的组织结构和分裂细胞。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。
一个由全角蛋白（PanCK）、CD10、α-平滑肌肌动蛋白（α-SMA）、钙视网膜蛋白、CD45、Ki67和4',6-二脒基-2-苯基吲哚（DAPI）组成的子宫内膜异位症特异性多重检测方法能够区分子宫内膜异位症中的组织结构。虽然机器学习方法能够可靠地分割组织亚结构，但对于细胞识别，基于深度学习的无需分割的算法更具优势。
局限性、谨慎的原因：本分析是在有限数量的样本上进行的，用于方法建立。为了进一步完善，应纳入对富含胶原蛋白的无细胞区域的量化，这可以进一步增强对纤维化变化程度的评估。此外，该方法应应用于更多样本以描绘亚型特异性差异。
我们证明了多重染色和细胞表型分析相结合在子宫内膜异位症研究中的巨大潜力。多重检测方法的优化程序是从一个与癌症相关的项目转移而来的，证明了该程序在癌症背景之外的稳健性。该检测方法可用于更大规模的批量分析。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，突显了该方法对异质性子宫内膜异位症样本的潜力。
研究资金/利益冲突：所有资金均通过部门资金提供。作者声明无利益冲突。
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