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DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
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DOI：10.1109/TMI.2016.2621185.png)
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Abstract
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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 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.
Keywords:
Bounding box
convolutional neural networks
DeepCut
image segmentation
machine learning
weak annotations
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