采用深层多发通道网络的新型管道通过荧光显微镜自动检测转移细胞的自主检测
A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy
影响因子:6.30000
分区:医学2区 / 数学与计算生物学1区 生物学2区 计算机：跨学科应用2区 工程：生物医学2区
发表日期:2024 Oct
作者:
Michail Mamalakis, Sarah C Macfarlane, Scott V Notley, Annica K B Gad, George Panoutsos
摘要
由癌细胞迁移驱动的转移是与癌症相关死亡的主要原因。它涉及细胞骨架组织的重大变化，其中包括肌动蛋白微丝和波形蛋白中间丝。了解这些细丝如何将细胞从正常变为侵入性会提供可用于改善癌症诊断和治疗的见解。我们已经开发了一种计算，透明，大规模和基于成像的管道，可以根据细胞细胞质中肌动蛋白和乙蛋白丝的空间组织来区分正常的人类细胞及其等生物匹配，造成的，侵入性和转移的对应物。由于这些亚细胞结构的复杂性，手动注释并不是自动化的。我们使用了已建立的深度学习方法和新的多发渠道体系结构。为了确保网络的高度解释性（对于应用领域至关重要），我们开发了一种可解释的全球可解释方法，该方法将总单元格图像的加权几何平均值及其本地GradCAM分数相关联。这些方法对细胞骨架的不同成分有详细，客观和可衡量
Abstract
Metastasis driven by cancer cell migration is the leading cause of cancer-related deaths. It involves significant changes in the organization of the cytoskeleton, which includes the actin microfilaments and the vimentin intermediate filaments. Understanding how these filament change cells from normal to invasive offers insights that can be used to improve cancer diagnosis and therapy. We have developed a computational, transparent, large-scale and imaging-based pipeline, that can distinguish between normal human cells and their isogenically matched, oncogenically transformed, invasive and metastasizing counterparts, based on the spatial organization of actin and vimentin filaments in the cell cytoplasm. Due to the intricacy of these subcellular structures, manual annotation is not trivial to automate. We used established deep learning methods and our new multi-attention channel architecture. To ensure a high level of interpretability of the network, which is crucial for the application area, we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The methods offer detailed, objective and measurable understanding of how different components of the cytoskeleton contribute to metastasis, insights that can be used for future development of novel diagnostic tools, such as a nanometer level, vimentin filament-based biomarker for digital pathology, and for new treatments that significantly can increase patient survival.Crown