Deep learning of circulating tumour cells

Leonie Laura Zeune, Yoeri Ewald Boink, Guus van Dalum, Afroditi Nanou, Sanne de Wit, K.C. Andree, Joost F. Swennenhuis, Stephan A. van Gils, L.W.M.M. Terstappen, Christoph Brune*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

46 Citations (Scopus)
517 Downloads (Pure)


Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. However, their use is currently hindered by their low frequency, tedious manual scoring and extensive cell heterogeneities. Those challenges limit the effectiveness of classical machine-learning methods for automated CTC analysis. Here, we combine autoencoding convolutional neural networks with advanced visualization techniques. This provides a very informative view on the data that opens the way for new biomedical research questions. We unravel hidden information in the raw image data of fluorescent images of blood samples enriched for CTCs. Our network classifies fluorescent images of single cells in five different classes with an accuracy, sensitivity and specificity of over 96%, and the obtained CTC counts predict the overall survival of cancer patients as well as state-of-the-art manual counts. Moreover, our network excelled in identifying different important subclasses of objects. Deep learning was faster and superior to classical image analysis approaches and enabled the identification of new biological phenomena.
Original languageEnglish
Pages (from-to)124-133
Number of pages10
JournalNature Machine Intelligence
Issue number2
Publication statusPublished - 10 Feb 2020


  • Applied Mathematics
  • Computer Science
  • Metastasis
  • Deep Learning
  • Neural Networks
  • Circulating tumor cells (CTCs)
  • Cancer
  • Semi-supervised learning
  • 22/2 OA procedure


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