Automated Multi-Process CTC Detection using Deep Learning

• URL: http://arxiv.org/abs/2109.12709v1
• Date: Sun, 26 Sep 2021 21:56:34 GMT
• Title: Automated Multi-Process CTC Detection using Deep Learning
• Authors: Elena Ivanova, Kam W. Leong, and Andrew F. Laine
• Abstract summary: We present a novel 3-stage detection model for automated identification of Circulating Tumor Cells. 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%.
• Score: 1.280824586913772
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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