  Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor
prognosis. However, the process of identification and later enumeration of CTCs
require manual labor, which is error-prone and time-consuming. The recent
developments in object detection via Deep Learning using Mask-RCNNs and wider
availability of pre-trained models have enabled sensitive tasks with limited
data of such to be tackled with unprecedented accuracy. In this report, we
present a novel 3-stage detection model for automated identification of
Circulating Tumor Cells in multi-channel darkfield microscopic images comprised
of: RetinaNet based identification of Cytokeratin (CK) stains, Mask-RCNN based
cell detection of DAPI cell nuclei and Otsu thresholding to detect CD-45s. The
training dataset is composed of 46 high variance data points, with 10 Negative
and 36 Positive data points. The test set is composed of 420 negative data
points. The final accuracy of the pipeline is 98.81%.
