Automatic Circulating Tumor Cell Segmentation and Enumeration in Digital Pathology by Using Deep Learning and Ball-scale Based Filtering Techniques

Abstract

Circulating tumor cells (CTCs) shed from the primary tumor, intravasate into blood, and translocate to distant tissues via circulation [1]. CTC enumeration allows cancer detection, treatment monitoring, and survival prediction [2], [3]. In the clinical setting immunofluorescence-based CTC enumeration is primarily used by expert cytopathologists. Manual enumeration requires cytopathologists with rich experience to read hundreds to thousands of images in hours. Despite the seemingly high number, this poor efficiency hinders the relevant clinical implementation. Therefore, high-automation enumeration is missing but highly desired [4]. Here, we proposed an automatic CTC segmentation and enumeration method in digital pathology by using deep learning techniques. To prepare for enumeration, peripheral blood mononuclear cells (PBMC) were extracted from cancer patient blood followed by infection with a reengineered adenovirus, i.e., rAdCTC, which is a CD46-targeting, DF3 promoter-selective, and GFP-overexpression adenovirus. The rAdCTC ensures detection specificity and efficiency of expression for CTCs. Subsequently, PBMCs were stained with anti-CD45 fluorescence-labeled antibody and DNA staining dye DAPI. Finally, the acquired fluorescence images were used for automatic segmentation and enumeration [5]. It is noteworthy that the fluorescence images used in this study contain three channels. The green, red, and blue signals respectively represent overexpressed GFP in infected cells, CD45 staining on leukocyte membranes, and nuclear staining.