发表机构
University of Oviedo; University Institute of Biotechnology of Asturias (IUBA)（阿斯图里亚斯大学; 阿斯图里亚斯生物技术大学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种计算机视觉流程，从荧光图像自动生成神经元分割掩膜，用于训练深度学习模型在相衬图像中无创识别神经元，并经一致性指标验证有效。
AI 中文摘要
背景与目标：在神经元培养物的显微镜图像中，准确分割神经元对于神经退行性疾病和神经毒性的研究至关重要。此类图像的人工标注耗时、主观且在不同专家间存在不一致性。深度学习（DL）模型提供了一种有效的替代方案，但需要由专家创建的高质量训练数据集，该数据集由具有准确分割神经元的显微镜图像组成。神经元培养物可通过相衬或荧光显微镜成像。虽然活神经元在荧光图像中易于检测，但该模态需要染色，这会影响细胞活力。相反，相衬成像无创但使神经元识别更具挑战性。方法：在本工作中，我们提出了一种稳健的计算机视觉流程，用于从荧光显微镜图像自动生成专家级神经元分割掩膜。这些掩膜用于训练DL模型，以在相衬图像中识别神经元。我们的方法依赖于在同一放大倍数和坐标下同时捕获的配对荧光和相衬图像，确保跨模态对齐。训练完成后，DL模型可单独分割相衬图像中的神经元，从而无需荧光染色。结果：我们通过使用多种一致性指标（包括IoU、准确率和Gwet's AC1）将自动生成的掩膜与专家标注进行比较来验证我们的方法。这些指标不仅针对实例的识别进行计算，还针对实例的分割进行计算。结论：本研究提出了一种经典的计算机视觉算法，用于生成神经元分割掩膜，可用于下游应用，如通过DL进行非侵入式细胞活力分析。
英文摘要
Background & Objective Accurate segmentation of neurons in microscopy images of neuronal cultures is crucial for research on neurodegenerative diseases and neurotoxicity. Manual annotation of such images is time-consuming, subjective, and inconsistent across experts. Deep learning (DL) models offer an effective alternative, but require high-quality training datasets composed of microscopy images with accurately segmented neurons, typically created by experts. Neuronal cultures can be imaged using either phase-contrast or fluorescence microscopy. While live neurons are easily detected in fluorescence images, this modality requires staining, which affects cell viability. Conversely, phase-contrast imaging is non-invasive but makes neuron identification more challenging. Methods In this work we present a robust computer vision pipeline for the automatic generation of expert-level neuron segmentation masks from fluorescence microscopy images. These masks are used to train DL models for neuron identification in phase-contrast images. Our method relies on paired fluorescence and phase-contrast images captured simultaneously at the same magnification and coordinates, ensuring alignment across modalities. Once trained, the DL models can segment neurons in phase-contrast images alone, eliminating the need for fluorescence staining. Results We validate our approach by comparing the automatically generated masks with expert annotations using multiple agreement metrics, including IoU, Accuracy, and Gwet's AC1. Such metrics are calculated not only for the identification but also for the segmentation of instances. Conclusions This study presents a classical computer vision algorithm for generating neuron segmentation masks, which can be used for downstream applications such as non-invasive cell viability analyses by means of DL.
