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Deep Learning For Multi-Task Medical Image Segmentation In Multiple Modalities

P. Moeskops, J. Wolterink, Bas H. M. van der Velden, K. Gilhuijs, T. Leiner, M. Viergever, I. Išgum
Published 2016 · Computer Science

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Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks.
This paper references
10.1007/978-3-319-24553-9_68
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
H. Roth (2015)
MICCAI 2012 workshop on multi-atlas labeling
B. A. Landman (2012)
10.1061/(ASCE)GT.1943-5606.0001284
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky (2012)
10.1109/TPAMI.2015.2437384
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
Ross B. Girshick (2016)
10.1117/12.2216971
2D image classification for 3D anatomy localization: employing deep convolutional neural networks
B. D. Vos (2016)
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan (2015)
10.1109/CVPR.2015.7298965
Fully convolutional networks for semantic segmentation
J. Long (2015)
10.1145/3065386
ImageNet classification with deep convolutional neural networks
A. Krizhevsky (2017)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert (2016)
10.1117/12.877233
Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes
Yefeng Zheng (2011)
sociation between parenchymal enhancement of the contralateral breast in dynamic contrast - enhanced MR imaging and outcome of patients with unilateral invasive breast cancer
B. H. van der Velden (2015)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe (2015)
10.1148/radiol.15142192
Association between Parenchymal Enhancement of the Contralateral Breast in Dynamic Contrast-enhanced MR Imaging and Outcome of Patients with Unilateral Invasive Breast Cancer.
Bas H. M. van der Velden (2015)
10.1007/978-3-642-40763-5_31
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
A. Prasoon (2013)
10.1162/jocn.2007.19.9.1498
Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults
D. Marcus (2007)
Adam: A Method for Stochastic Optimization
Diederik P. Kingma (2015)
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava (2014)
10.1007/978-3-642-33418-4_46
Segmentation of the Pectoral Muscle in Breast MRI Using Atlas-Based Approaches
A. Gubern-Mérida (2012)
10.1109/TMI.2016.2548501
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.
P. Moeskops (2016)
10.1007/978-3-319-24553-9_72
Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks
Jelmer M. Wolterink (2015)
10.1109/CVPRW.2015.7301312
Deep neural networks for anatomical brain segmentation
A. D. Brébisson (2015)



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10.1109/ACCESS.2019.2908386
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AMCNet: Attention-Based Multiscale Convolutional Network for DCM MRI Segmentation
C. Luo (2019)
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Edoardo Giacomello (2019)
10.1088/1361-6560/ab0ef4
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X. Li (2019)
10.1007/s11548-018-1856-x
FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images
M. Villa (2018)
10.1016/j.media.2017.07.005
A survey on deep learning in medical image analysis
G. Litjens (2017)
Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
Umaseh Sivanesan (2019)
10.3322/caac.21552
Artificial intelligence in cancer imaging: Clinical challenges and applications
W. Bi (2019)
10.1007/s11042-018-6463-x
A brief review on multi-task learning
Kim-Han Thung (2018)
10.1007/s10278-019-00245-9
Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features
V. Srivastava (2019)
10.1007/978-3-030-33391-1_23
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Chengliang Dai (2019)
10.1007/978-3-030-13969-8_4
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Yingwei Li (2019)
On segmentation of pectoralis muscle in digital mammograms by means of deep learning
Hossein Soleimani (2020)
Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty
Rebecca Nichole Mahon (2018)
10.1002/mp.13221
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Yabo Fu (2018)
10.1007/978-3-319-94878-2_20
Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists
E. Ranschaert (2019)
10.1002/mp.13375
An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets
H. Fashandi (2019)
10.1109/WACV.2018.00066
Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI
V. Valindria (2018)
10.1186/s12859-019-3037-5
Towards pixel-to-pixel deep nucleus detection in microscopy images
F. Xing (2019)
10.1016/j.mri.2019.06.009
Role of deep learning in infant brain MRI analysis.
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10.1007/978-3-030-36189-1_24
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10.1007/978-3-030-33904-3_23
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