Segmentation of intracranial arterial calcification with deeply supervised residual dropout networks
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Segmentation of intracranial arterial calcification with deeply supervised residual dropout networks. / Bortsova, Gerda; van Tulder, Gijs; Dubost, Florian; Peng, Tingying; Navab, Nassir; van der Lugt, Aad; Bos, Daniel; de Bruijne, Marleen.
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. ed. / Maxime Descoteaux; Lena Maier-Hein; Alfred Franz; Pierre Jannin; D. Louis Collins; Simon Duchesne. Springer, 2017. p. 356-364 (Lecture notes in computer science, Vol. 10435).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Segmentation of intracranial arterial calcification with deeply supervised residual dropout networks
AU - Bortsova, Gerda
AU - van Tulder, Gijs
AU - Dubost, Florian
AU - Peng, Tingying
AU - Navab, Nassir
AU - van der Lugt, Aad
AU - Bos, Daniel
AU - de Bruijne, Marleen
N1 - Conference code: 20
PY - 2017
Y1 - 2017
N2 - Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annotation of ICAC hampers much demanded further research into the relationship between ICAC and neurological diseases. Automation of ICAC segmentation is therefore highly desirable, but difficult due to the proximity of the lesions to bony structures with a similar attenuation coefficient. In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge. Our method is based on a 3D fully convolutional neural network that we extend with two regularization techniques. Firstly, we use deep supervision to encourage discriminative features in the hidden layers. Secondly, we augment the network with skip connections, as in the recently developed ResNet, and dropout layers, inserted in a way that skip connections circumvent them. We investigate the effect of skip connections and dropout. In addition, we propose a simple problem-specific modification of the network objective function that restricts the focus to the most important image regions and simplifies the optimization. We train and validate our model using 882 CT scans and test on 1,000. Our regularization techniques and objective improve the average Dice score by 7.1%, yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC volumes and manual annotations.
AB - Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annotation of ICAC hampers much demanded further research into the relationship between ICAC and neurological diseases. Automation of ICAC segmentation is therefore highly desirable, but difficult due to the proximity of the lesions to bony structures with a similar attenuation coefficient. In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge. Our method is based on a 3D fully convolutional neural network that we extend with two regularization techniques. Firstly, we use deep supervision to encourage discriminative features in the hidden layers. Secondly, we augment the network with skip connections, as in the recently developed ResNet, and dropout layers, inserted in a way that skip connections circumvent them. We investigate the effect of skip connections and dropout. In addition, we propose a simple problem-specific modification of the network objective function that restricts the focus to the most important image regions and simplifies the optimization. We train and validate our model using 882 CT scans and test on 1,000. Our regularization techniques and objective improve the average Dice score by 7.1%, yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC volumes and manual annotations.
KW - Calcium scoring
KW - Deep learning
KW - Deep supervision
KW - Dropout
KW - Intracranial calcifications
KW - Residual networks
U2 - 10.1007/978-3-319-66179-7_41
DO - 10.1007/978-3-319-66179-7_41
M3 - Article in proceedings
AN - SCOPUS:85029535684
SN - 978-3-319-66178-0
T3 - Lecture notes in computer science
SP - 356
EP - 364
BT - Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
A2 - Descoteaux, Maxime
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Duchesne, Simon
PB - Springer
Y2 - 11 September 2017 through 13 September 2017
ER -
ID: 184143404