Automated Multi-Process CTC Detection using Deep Learning

TL;DR

This paper introduces a novel three-stage deep learning pipeline for automated detection of Circulating Tumor Cells in microscopic images, significantly reducing manual effort and improving accuracy.

Contribution

It presents a new multi-process detection model combining RetinaNet, Mask-RCNN, and Otsu thresholding for CTC identification in darkfield microscopy images.

Findings

Achieved 98.81% accuracy in CTC detection

Successfully integrated multiple deep learning models for complex image analysis

Reduced manual labor in CTC enumeration

Abstract

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%.