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An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12069)

Abstract

Facilitating quantitative analysis of cytology images of fine needle aspirates of uveal melanoma is important to confirm diagnosis and inform management decisions. Extracting high-quality regions of interest (ROIs) from cytology whole slide images is a critical first step. To the best of our knowledge, we describe the first unsupervised clustering-based method for fine needle aspiration cytology (FNAC) that automatically suggests high-quality ROIs. Our method is integrated in a graphical user interface that allows for interactive refinement of ROI suggestions to tailor analysis to any specific specimen. We show that the proposed approach suggests ROIs that are in very good agreement with expert-extracted regions and demonstrate that interactive refinement results in the extraction of more high-quality regions compared to purely algorithmic extraction alone.

Keywords

  • Human-computer interaction
  • Unsupervised learning
  • Machine learning
  • Coarse to fine

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Acknowledgement

We gratefully acknowledge funding from the Emerson Collective Cancer Research Fund and internal funds provided by the Wilmer Eye Institute and the Malone Center for Engineering in Healthcare at Johns Hopkins University.

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Correspondence to Haomin Chen .

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Chen, H., Liu, T.Y.A., Correa, Z., Unberath, M. (2020). An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-63419-3_12

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