Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rajchl:2016:10.1109/TMI.2016.2621185,
author = {Rajchl, M and Lee, MCH and Oktay, O and Kamnitsas, K and Passerat-Palmbach, J and Bai, W and Damodaram, M and Rutherford, MA and Hajnal, JV and Kainz, B and Rueckert, D},
doi = {10.1109/TMI.2016.2621185},
journal = {IEEE Transactions on Medical Imaging},
pages = {674--683},
title = {DeepCut: object segmentation from bounding box annotations using convolutional neural networks},
url = {http://dx.doi.org/10.1109/TMI.2016.2621185},
volume = {36},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Rajchl,M
AU  - Lee,MCH
AU  - Oktay,O
AU  - Kamnitsas,K
AU  - Passerat-Palmbach,J
AU  - Bai,W
AU  - Damodaram,M
AU  - Rutherford,MA
AU  - Hajnal,JV
AU  - Kainz,B
AU  - Rueckert,D
DO  - 10.1109/TMI.2016.2621185
EP  - 683
PY  - 2016///
SN  - 0278-0062
SP  - 674
TI  - DeepCut: object segmentation from bounding box annotations using convolutional neural networks
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2016.2621185
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000396115800030&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/45459
VL  - 36
ER  -
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