Top

The Journal of Supercomputing

Published in:

26-08-2020

DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service

Authors: Kuo-Kun Tseng, Ran Zhang, Chien-Ming Chen, Mohammad Mehedi Hassan

Published in: The Journal of Supercomputing | Issue 4/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Deep learning approaches have achieved good performance in segmenting medical images. In this paper, we propose a new convolutional neural network architecture named DNetUnet, which combines U-Nets with different down-sampling levels and a new dense block as feature extractor. In addition, DNetUnet is a semi-supervised learning method, which can be used not only to obtain expert knowledge from the labelled corpus, but also to enhance the performance of learning algorithm generalization ability from unlabelled data. Further, we integrate distillation technique to improve the performance on mobile platform. The experimental results demonstrate that the proposed segmentation model yields superior performance over competition. Since the processing of large medical images and distillation technology is enforced, a supercomputing AI training server is a preference for its application.

previous article Network intrusion detection using multi-architectural modular deep neural network

next article A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Eq. (15).

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems

Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

Tran PV (2016) A fully convolutional neural network for cardiac segmentation in short-axis MRI

Lieman-Sifry J, Le M, Lau F et al (2017) FastVentricle: cardiac segmentation with ENet

Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495CrossRef

Chen LC, Papandreou G, Kokkinos I et al (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput Sci 2014(4):357–361

Chen LC, Papandreou G, Kokkinos I et al (2016) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848CrossRef

Chen L C, Papandreou G, Schroff F et al (2017) Rethinking atrous convolution for semantic image segmentation

Lin G, Milan A, Shen C et al (2017) RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

10.

Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRef

11.

Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention

12.

Baumgartner CF, Koch LM, Pollefeys M et al (2017) An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In: International Workshop on Statistical Atlases and Computational Models of the Heart

13.

Jang Y, Hong Y, Ha S et al (2017) Automatic segmentation of LV and RV in cardiac MRI. In: International Workshop on Statistical Atlases and Computational Models of the Heart

14.

Patravali J, Jain S, Chilamkurthy S (2017) 2D-3D fully convolutional neural networks for cardiac MR segmentation. In: International Workshop on Statistical Atlases and Computational Models of the Heart

15.

Isensee F, Jaeger PF, Full PM et al (2017) Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: International Workshop on Statistical Atlases and Computational Models of the Heart

16.

Khened M, Alex V, Krishnamurthi G (2017) Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest. In: International Workshop on Statistical Atlases and Computational Models of the Heart

17.

Marc-Michel Rohé, Datar M, Heimann T et al (2017) SVF-Net: learning deformable image registration using shape matching. In: International Conference on Medical Image Computing and Computer-Assisted Intervention

18.

Zotti C, Luo Z, Lalande A et al (2017) GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

19.

He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition

20.

Gao H, Zhuang L, Maaten LVD et al (2017) Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition

21.

Zhou Z, Siddiquee M M R, Tajbakhsh N et al (2018) UNet ++: a nested U-net architecture for medical image segmentation

22.

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on International Conference on Machine Learning

23.

Sudre C H, Li W, Vercauteren T et al (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations

24.

Kai H, Zhang Z, Niu X et al (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:S0925231218305423

25.

Huttenlocher DP, Klanderman GA, Rucklidge WA (1993) Comparing images using the hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863CrossRef

26.

Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 99:2999–3007

27.

Mangalam K, Salzamann M (2018) On compressing U-net using knowledge distillation. arXiv preprint arXiv:1812.00249

28.

Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations

Title: DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service
Authors: Kuo-Kun Tseng
Ran Zhang
Chien-Ming Chen
Mohammad Mehedi Hassan
Publication date: 26-08-2020
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 4/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03407-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 4/2021

Two-phase node deployment for target coverage in rechargeable WSNs using genetic algorithm and integer linear programming

Suspicious activity detection using deep learning in secure assisted living IoT environments

Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning

An improved model for predicting trip mode distribution using convolution deep learning

An efficient management scheme of blockchain-based cloud user information using probabilistic weighting

A performance- and energy-oriented extended tuning process for time-step-based scientific applications

Premium Partner