Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.

中文翻译：

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基于机器学习的CTC图像识别新方法

源自原发肿瘤和/或转移性肿瘤的循环肿瘤细胞 (CTC) 是肿瘤预后的标志物，也可用于监测治疗效果和肿瘤复发。循环肿瘤细胞的富集和筛选可以自动化，但 CTC 的最终计数目前需要人工干预。这不仅需要有经验的病理学家的参与，而且容易造成人为的误判。基于机器学习的医学图像识别可以有效减少工作量，提高自动化水平。因此，我们使用机器学习来识别 CTC。首先，我们收集了 600 名患者的 CTC 检测结果。免疫荧光染色后，每张图片呈现一个阳性 CTC 细胞核和几个阴性对照。然后通过图像去噪对 CTC 的图像进行分割，使用python的openCV方案进行图像过滤、边缘检测、图像扩展和收缩技术。随后，使用传统的图像识别方法和机器学习来识别 CTC。机器学习算法使用卷积神经网络深度学习网络进行训练。我们从 600 名患者身上提取了 2300 个细胞用于训练和测试。大约 1300 个细胞用于训练，其他细胞用于测试。识别的敏感性和特异性分别达到90.3%和91.3%。我们将进一步修改我们的模型，希望达到更高的灵敏度和特异性。机器学习算法使用卷积神经网络深度学习网络进行训练。我们从 600 名患者身上提取了 2300 个细胞用于训练和测试。大约 1300 个细胞用于训练，其他细胞用于测试。识别的灵敏度和特异性分别达到90.3%和91.3%。我们将进一步修改我们的模型，希望达到更高的灵敏度和特异性。机器学习算法使用卷积神经网络深度学习网络进行训练。我们从 600 名患者身上提取了 2300 个细胞用于训练和测试。大约 1300 个细胞用于训练，其他细胞用于测试。识别的敏感性和特异性分别达到90.3%和91.3%。我们将进一步修改我们的模型，希望达到更高的灵敏度和特异性。