• DOI:10.1109/TMI.2016.2621185
Corpus ID: 10726144

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

@article{Rajchl2017DeepCutOS,
  title={DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks},
  author={Martin Rajchl and M. J. Lee and O. Oktay and K. Kamnitsas and J. Passerat-Palmbach and Wenjia Bai and Bernhard Kainz and D. Rueckert},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={674-683}
}

Martin Rajchl, M. J. Lee, +5 authors D. Rueckert

Published 2017

Computer Science, Medicine

IEEE Transactions on Medical Imaging

In this paper, we propose <italic>DeepCut</italic>, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. [...] Key Method It extends the approach of the well-known <italic>GrabCut</italic> <xref ref-type="bibr" rid="ref1">[1]</xref> method to include machine learning by training a neural network classifier from bounding box annotations.Expand Abstract

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