Baral Sushish, Paing May Phu
Department of Robotics and AI, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Quant Imaging Med Surg. 2024 Sep 1;14(9):6204-6221. doi: 10.21037/qims-24-801. Epub 2024 Aug 28.
Light microscopy is a widely used technique in cell biology due to its satisfactory resolution for cellular structure analysis, prevalent availability of fluorescent probes for staining, and compatibility for the dynamic analysis of live cells. However, the segmentation of cells and nuclei from microscopic images is not a straightforward process because it has several challenges such as high variation in morphology and shape, the presence of noise and diverse contrast in backgrounds, clustering or overlapping nature of cells. Dealing with these challenges and facilitating more reliable analysis necessitates the implementation of computer-aided methods that leverage image processing techniques and deep learning algorithms. The major goal of this study is to propose a model, for instance segmentation of cells and nuclei, applying the most cutting-edge deep learning techniques.
A fine-tuned You Only Look at Once version 9 extended (YOLOv9-E) model is initially applied as a prompt generator to generate bounding box prompts. Using the generated prompts, a pre-trained segment anything model (SAM) is subsequently applied through zero-short inferencing to produce raw segmentation masks. These segmentation masks are then refined using non-max suppression and simple image processing methods such as image addition and morphological processing. The proposed method is developed and evaluated using an open-sourced dataset called Expert Visual Cell Annotation (EVICAN), which is relatively large and contains 4,738 microscopy images extracted from cross organs using different protocols.
Based on the evaluation results on three different levels of EVICAN test sets, the proposed method demonstrates noticeable performances showing average mAP50 [mean average precision at intersection over union (IoU) =0.50] scores of 96.25, 95.05, and 94.18 for cell segmentation, and 68.04, 54.66, and 38.29 for nucleus segmentation on easy, medium, and difficult test sets, respectively.
Our proposed method for instance segmentation of cells and nuclei provided favorable performance compared to the existing methods in literature, indicating its potential utility as an assistive tool for cell culture experts, facilitating prompt and reliable analysis.
光学显微镜在细胞生物学中是一种广泛使用的技术，因为它在细胞结构分析方面具有令人满意的分辨率，有普遍可用的荧光探针用于染色，并且适用于活细胞的动态分析。然而，从显微镜图像中分割细胞和细胞核并非易事，因为它面临诸多挑战，如形态和形状的高度变化、噪声的存在、背景中的多种对比度、细胞的聚集或重叠性质。应对这些挑战并促进更可靠的分析需要实施利用图像处理技术和深度学习算法的计算机辅助方法。本研究的主要目标是提出一种模型，用于细胞和细胞核的实例分割，应用最前沿的深度学习技术。
首先应用一个微调的第九版扩展你只看一次（YOLOv9-E）模型作为提示生成器来生成边界框提示。使用生成的提示，随后通过零样本推理应用预训练的分割一切模型（SAM）来生成原始分割掩码。然后使用非极大值抑制和简单的图像处理方法（如图像相加和形态学处理）对这些分割掩码进行细化。所提出的方法是使用一个名为专家视觉细胞注释（EVICAN）的开源数据集开发和评估的，该数据集相对较大，包含使用不同协议从多个器官中提取的4738张显微镜图像。
基于在EVICAN测试集的三个不同级别上的评估结果，所提出的方法表现出色，在简单、中等和困难测试集上，细胞分割的平均mAP50（交并比（IoU）=0.50时的平均平均精度）分数分别为96.25、95.05和94.18，细胞核分割的分数分别为68.04、54.66和38.29。
我们提出的细胞和细胞核实例分割方法与文献中的现有方法相比表现良好，表明其作为细胞培养专家辅助工具的潜在效用，有助于进行快速可靠的分析。
