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 en-
ergy 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 na
̈
ıve 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.