An integrated iterative annotation technique for easing neural network training in medical image analysis

Brendon Lutnick, Brandon Ginley, Darshana Govind, Sean D. McGarry, Peter S. LaViolette, Rabi Yacoub, Sanjay Jain, John E. Tomaszewski, Kuang Yu Jen, Pinaki Sarder

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a ‘human-in-the-loop’ to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.

Original languageEnglish (US)
Pages (from-to)112-119
Number of pages8
JournalNature Machine Intelligence
Volume1
Issue number2
DOIs
StatePublished - Feb 1 2019

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Software

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    Lutnick, B., Ginley, B., Govind, D., McGarry, S. D., LaViolette, P. S., Yacoub, R., Jain, S., Tomaszewski, J. E., Jen, K. Y., & Sarder, P. (2019). An integrated iterative annotation technique for easing neural network training in medical image analysis. Nature Machine Intelligence, 1(2), 112-119. https://doi.org/10.1038/s42256-019-0018-3