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

Research output: Contribution to journalArticle

Standard

DeepCut : Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks. / Rajchl, Martin; Lee, Matthew C H; Oktay, Ozan; Kamnitsas, Konstantinos; Passerat-Palmbach, Jonathan; Bai, Wenjia; Damodaram, Mellisa; Rutherford, Mary A.; Hajnal, Joseph V.; Kainz, Bernhard; Rueckert, Daniel.

In: IEEE Transactions on Medical Imaging, Vol. 36, No. 2, 7739993, 01.02.2017, p. 674-683.

Research output: Contribution to journalArticle

Harvard

Rajchl, M, Lee, MCH, Oktay, O, Kamnitsas, K, Passerat-Palmbach, J, Bai, W, Damodaram, M, Rutherford, MA, Hajnal, JV, Kainz, B & Rueckert, D 2017, 'DeepCut: Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks', IEEE Transactions on Medical Imaging, vol. 36, no. 2, 7739993, pp. 674-683. https://doi.org/10.1109/TMI.2016.2621185

APA

Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., ... Rueckert, D. (2017). DeepCut: Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging, 36(2), 674-683. [7739993]. https://doi.org/10.1109/TMI.2016.2621185

Vancouver

Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W et al. DeepCut: Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging. 2017 Feb 1;36(2):674-683. 7739993. https://doi.org/10.1109/TMI.2016.2621185

Author

Rajchl, Martin ; Lee, Matthew C H ; Oktay, Ozan ; Kamnitsas, Konstantinos ; Passerat-Palmbach, Jonathan ; Bai, Wenjia ; Damodaram, Mellisa ; Rutherford, Mary A. ; Hajnal, Joseph V. ; Kainz, Bernhard ; Rueckert, Daniel. / DeepCut : Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks. In: IEEE Transactions on Medical Imaging. 2017 ; Vol. 36, No. 2. pp. 674-683.

Bibtex Download

@article{2b8f91451a8e41ce8df6bcce9c6a45d1,
title = "DeepCut: Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks",
abstract = "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 na{\"i}ve 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",
author = "Martin Rajchl and Lee, {Matthew C H} and Ozan Oktay and Konstantinos Kamnitsas and Jonathan Passerat-Palmbach and Wenjia Bai and Mellisa Damodaram and Rutherford, {Mary A.} and Hajnal, {Joseph V.} and Bernhard Kainz and Daniel Rueckert",
year = "2017",
month = "2",
day = "1",
doi = "10.1109/TMI.2016.2621185",
language = "English",
volume = "36",
pages = "674--683",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
number = "2",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - DeepCut

T2 - Object Segmentation from Bounding Box Annotations Using Convolutional Neural Networks

AU - Rajchl, Martin

AU - Lee, Matthew C H

AU - Oktay, Ozan

AU - Kamnitsas, Konstantinos

AU - Passerat-Palmbach, Jonathan

AU - Bai, Wenjia

AU - Damodaram, Mellisa

AU - Rutherford, Mary A.

AU - Hajnal, Joseph V.

AU - Kainz, Bernhard

AU - Rueckert, Daniel

PY - 2017/2/1

Y1 - 2017/2/1

N2 - 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.

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 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.

KW - Bounding box

KW - convolutional neural networks

KW - DeepCut

KW - image segmentation

KW - machine learning

KW - weak annotations

UR - http://www.scopus.com/inward/record.url?scp=85012110429&partnerID=8YFLogxK

U2 - 10.1109/TMI.2016.2621185

DO - 10.1109/TMI.2016.2621185

M3 - Article

AN - SCOPUS:85012110429

VL - 36

SP - 674

EP - 683

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 2

M1 - 7739993

ER -

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