Manual detection of ColoRectal Cancers (CRC) is subject to bias and time-consuming. To be objective and efficient, this study proposes a semiautomated method to predict CRC based on the number of Circulating Tumor Cells (CTCs). To count CTCs, this method uses SVM to find possible CTCs using features such as cell size, the ratio of the image area of the nucleus to that of the cytoplasm, and the color intensity distribution of the nucleus and cytoplasm. We propose the wrapping degree to which cytoplasm wraps the nucleus after SVM to significantly improve the results. The area under the receiver operating characteristic curve for CRC prediction performance was 0.78 for manual identification and 0.74 for our semiautomated method. This indicates that semiautomated identification was nearly as accurate in predicting CRC as manual identification. Our semiautomated method only required 20–40 min for detecting 400 CTCs, which was much faster than manual identification.

手动检测结肠直肠癌 (CRC) 存在偏差且耗时。为了客观和高效，本研究提出了一种基于循环肿瘤细胞 (CTC) 数量预测 CRC 的半自动化方法。为了对 CTC 进行计数，该方法使用 SVM 使用诸如细胞大小、细胞核与细胞质图像区域的比率以及细胞核和细胞质的颜色强度分布等特征来寻找可能的 CTC。我们提出了 SVM 后细胞质包裹细胞核的包裹程度，以显着改善结果。CRC 预测性能的接收者操作特征曲线下面积对于手动识别为 0.78，对于我们的半自动方法为 0.74。这表明半自动识别在预测 CRC 方面几乎与手动识别一样准确。