Detection of Circulating Tumor Cells in Blood Using Two-Step Random Forest

Abstract

Cancer has been the leading cause of death among Japanese since 1981, and many people die from it every year worldwide. While various measures have been taken to reduce the mortality rate of cancer, circulating tumor cells (CTCs) in the blood have been attracting attention in recent years. In the past, CTCs were detected by visual inspection by a physician or by an expensive machine, but these methods required much effort by the physician and required only EpCAM-expressing cells to be detected. In addition, detection by image processing has been used, but it has the problem that the area of interest is only a part of the area and there are many false positives. In this paper, we propose a two-step classification method that focuses on the shape and surface of cells. In the proposed method, multiple shape and surface features are obtained for four types of cells in blood images: Clusters, CTCs, Normal Cells, and Vertical Cells. Based on the features, cells are classified using a two-step Random Forest and their accuracy is evaluated. Furthermore, the effectiveness of the proposed method is demonstrated by comparing it with conventional methods.