在扫描电子显微镜图像中增强细胞实例细分通过深度轮闭合操作员进行扫描
Florian Robert1, Alexia Calovoulos2, Laurent Facq3
1Univ. of Bordeaux, CNRS, Institut de Mathématiques de Bordeaux, IMB, UMR5251, 351 cours de la Libération, Talence, F-33400, France; INRIA Bordeaux, MONC team, 200 avenue de la Vieille Tour, Talence, F-33400, France; Univ. Bordeaux, INSERM, Bordeaux Institute in Oncology, BRIC, U1312, MIRCADE team, 146 rue Léo Saignat, Bordeaux, 33000, France.
Computers in biology and medicine
|April 2, 2025
概括
这项研究引入了一种新的AI方法,COp-Net,通过填补细胞边界的空白来准确地细分扫描电子显微镜图像中的细胞. 这大大提高了细胞细分的准确性,并减少了癌症研究中手动校正时间.
科学领域:
- 在瘤学瘤学.
- 生物成像是一种生物成像.
- 计算生物学 计算生物学
背景情况:
- 在扫描电子显微镜 (SEM) 图像中精确的细胞细分对于理解瘤学中的组织结构至关重要.
- 目前用于SEM图像中细胞细分的AI方法经常产生错误,特别是在低质量的区域,需要大量的手动校正.
- 细胞轮划分的缺陷阻碍了SEM数据中细胞结构的精确分析.
研究的目的:
- 开发一种新的AI驱动的方法来改进SEM图像中的细胞边界划分.
- 通过解决细胞轮中的差距来提高基于实例的细胞细分精度.
- 为了减少在SEM图像分析中对瘤学的手动校正的需求.
主要方法:
- 引入一个卷积神经网络 (CNN) 关闭操作员 (COp-Net),旨在填补细胞轮的空白.
- 使用部分微分方程 (PDE) 来生成低完整度的概率图,以克服训练数据的限制.
- 使用来自患者衍生的异种移植 (PDX) 肝细胞瘤组织和公共数据集的私人SEM图像验证COp-Net.
主要成果:
- COp-Net在准确划定细胞边界方面取得了显著的改进.
- 与最先进的方法相比,在私人数据上,精确划分的单元增加了约50%,在公共数据上增加了10%.
- 显著减少了手动校正的必要性,从而加速了数字化过程.
结论:
- 拟议的COp-Net有效地提高了细胞实例细分的准确性,特别是在具有受损细胞边界的挑战性SEM图像区域.
- 这种人工智能驱动的填补差距的方法有助于在瘤切除术领域详细研究瘤组织生物架构.
- 公共可用性COp-Net权重和PDE源代码促进可重复性和进一步研究.
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