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 Matthew C. H. Lee and Ozan Oktay and Konstantinos Kamnitsas and Jonathan Passerat-Palmbach and Wenjia Bai and Bernhard Kainz and Daniel Rueckert},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={674-683}
}
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. 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. We formulate the problem as an energy minimisation problem over a densely-connected conditional random… CONTINUE READING

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