Abstract
Robust detection and numeration of circulating tumor cells (CTCs) in scanned microscopic images of peripheral blood is essential to clinical diagnosis, individualized treatment, and prognosis judgement evaluation. Automated detection algorithm based on machine learning methods helps to reduce the subjectivity and labor intensity of cytologists in their clinical practice. In this paper, a robust CTCs detection algorithm based on cascading of two stages of detectors is proposed. Firstly, a morphological rule based detector is applied to screen out most normal cells. Based on the detection results of the first stage detection, hard negative sample selection is performed according to confidence score values of an integrated deep classifier. Finally, a second stage faster R-CNN detector is trained on positive and negative samples to obtain the detection results. Experimental results carried out on a self-established CTCs database show that the proposed algorithm achieves robust and quasi-realtime CTCs detection.
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Liu, YX., Zhang, AJ., Meng, QF., Chen, YJ., Yang, Y., Chen, YH. (2019). Robust Circulating Tumor Cells Detection in Scanned Microscopic Images with Cascaded Morphological and Faster R-CNN Deep Detectors. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_70
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DOI: https://doi.org/10.1007/978-3-030-26969-2_70
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