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计算机视觉可以预测重新内皮化小鼠肺部的细胞播种覆盖率.

Joshua Paciocco¹, Ahmed Hasan¹, Jason Chan¹

¹Department of Mechanical and Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada.

Scientific reports

|July 19, 2025

概括

使用语义细分的组织图像的自动分析可以准确地量化细胞播种覆盖 (CSC) 在重新细胞化的肺部. 基于补丁的U-Net模型实现了高精度,改善了肺移植研究.

关键词:

生物工程制造的肺部细胞播种覆盖范围细胞播种覆盖肺部重新内皮质化的肺部.机器学习机器学习医学图像医学图像医学图像语义细分语义细分语义细分

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科学领域:

生物医学工程生物医学工程
再生医学是一种再生医学.
计算病理学计算病理学

背景情况:

肺移植的目的是通过重新细胞化的移植来减少排斥,但实现足够的细胞覆盖面是具有挑战性的.
细胞播种覆盖率 (CSC) 是评估再细胞化疗效的关键指标,需要精确量化肺支架和播种细胞区域.
目前用于CSC的组织图像分析是人工和耗时的,限制了可扩展性和可重现性.

研究的目的:

调查语义细分模型 (U-Net和LinkNet) 对于组织学图像自动像素分析的有效性.
准确量化肺支架和播种细胞面积,用于计算细胞播种覆盖率 (CSC).
为了比较U-Net和LinkNet模型的性能,包括对完整图像和图像补丁的培训.

主要方法:

将U-Net和LinkNet语义细分模型应用于重新内皮化小鼠肺部的组织图像.
肺支架和种植细胞区域的像素智能分类,以实现自动化CSC计算.
基于培训策略的模型性能比较分析 (完整图像与图像补丁).

主要成果:

基于补丁的U-Net模型在预测CSC方面表现出卓越的性能,达到2.23±0.36%的根平均平方误差.
获得了高交叉与结合 (IoU) 评分,用于分类肺支架 (77.8 ± 1.4%) 和播种细胞像素 (69.5 ± 1.1%).
与手工方法相比,自动化分析显著提高了CSC量化的准确性和效率.

结论:

语义细分,特别是基于补丁的U-Net模型,提供了一种强大而准确的方法来量化肺复细胞化中的细胞播种覆盖率.
这种自动化方法有助于评估再细胞化疗效,有可能加速肺移植研究的进展.
精确的CSC测量对于开发改进的策略来减少移植拒绝和移植后并发症至关重要.