器官样品提供了一个强大的模型，用于研究细胞自我组织、体外特定组织形态的生长以及评估潜在的医学疗法。然而，这些系统的内在机制尚未完全理解，这可能导致器官样品因培养条件和基底膜提取物的差异而产生可变性。改善器官样品培养的标准化对其在临床方案中的应用至关重要。开发工具来评估和预测这些系统的行为可能产生更强大和标准化的生物模型，以进行准确的临床研究。在这里，我们开发了一个算法，用于自动计数体外和体内肠道器官样品的类隐结构。此外，我们修改了现有的二维基于代理的数学模型，以更好地描述肠道器官样品的系统生理，并评估其与我们生成的新实验数据相比，复制成瘤结构的能力。类隐结构的计数算法在近似于培养于种植后第3天和第7天的体外肠道器官样品图像中找到的平均成瘤结构方面证明了其有用性。我们对体外模型的改变保持了产生与体外数据第5天和第7天发现的成瘤结构数量相符的模拟结果的潜力。本研究旨在帮助量化关键形态结构，并为比较体外和体内实验提供一种方法。我们的结果将来可以扩展为三维体外模型。 版权：© 2023 Montes-Olivas等。这是一篇开放获取文章，根据创作共用许可证分发，允许在任何媒介上自由使用、分发和复制，前提是保留原作者和来源的信息。

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.Copyright: © 2023 Montes-Olivas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.