DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Martin Rajchl, Matthew Chung Hai Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat‐Palmbach, Wenjia Bai, Mellisa S. Damodaram, Mary Rutherford, Joseph V. Hajnal, Bernhard Kainz, Daniel Rueckert · IEEE Transactions on Medical Imaging · 2016
In this paper, we propose DeepCut, 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 GrabCut [1] 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 field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.