DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

Martin Rajchl; Matthew C. H. Lee; Ozan Oktay; Konstantinos Kamnitsas; Jonathan Passerat-Palmbach; Wenjia Bai; Mellisa Damodaram; Mary A. Rutherford; Joseph V. Hajnal; Bernhard Kainz; Daniel Rueckert

arXiv · 1605.07866

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

Martin Rajchl·Matthew C. H. Lee·Ozan Oktay·Konstantinos Kamnitsas·Jonathan Passerat-Palmbach·Wenjia Bai·Mellisa Damodaram·Mary A. Rutherford·Joseph V. Hajnal·Bernhard Kainz·Daniel Rueckert

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Abstract

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut 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 naive 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.

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Martin Rajchl, Matthew C. H. Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat-Palmbach, Wenjia Bai, Mellisa Damodaram, Mary A. Rutherford, Joseph V. Hajnal, Bernhard Kainz, Daniel Rueckert. 2016-06-05. DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks. https://arxiv.org/abs/1605.07866

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