Article

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Authors:

Zongwei Zhou,

Md Mahfuzur Rahman Siddiquee,

Nima Tajbakhsh,

Jianming LiangAuthors Info & Claims

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

Pages 3 - 11

https://doi.org/10.1007/978-3-030-00889-5_1

Published: 20 September 2018 Publication History

Abstract

In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.

References

[1]

Armato SG et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans Med. Phys. 2011 38 2 915-931

Crossref

Google Scholar

[2]

Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C, et al. Carneiro G et al. The importance of skip connections in biomedical image segmentation Deep Learning and Data Labeling for Medical Applications 2016 Cham Springer 179-187

Crossref

Google Scholar

[3]

Fourure, D., Emonet, R., Fromont, E., Muselet, D., Tremeau, A., Wolf, C.: Residual conv-deconv grid network for semantic segmentation. arXiv preprint arXiv:1707.07958 (2017)

Google Scholar

[4]

He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)

Google Scholar

[5]

Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)

Google Scholar

[6]

Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570 (2015)

Google Scholar

[7]

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes. arXiv preprint arXiv:1709.07330 (2017)

Google Scholar

[8]

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

Google Scholar

[9]

Ronneberger O, Fischer P, and Brox T Navab N, Hornegger J, Wells WM, and Frangi AF U-Net: convolutional networks for biomedical image segmentation Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 2015 Cham Springer 234-241

Crossref

Google Scholar

[10]

Tajbakhsh N et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 2016 35 5 1299-1312

Crossref

Google Scholar

[11]

Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M., Liang, J.: Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7340–7351 (2017)

Google Scholar

Cited By

View all

Brar KGoyal BDogra AMustafa MMajumdar RAlkhayyat AKukreja V(2025)Image segmentation reviewInformation Fusion10.1016/j.inffus.2024.102608114:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.inffus.2024.102608
Durasov NOner DDonier JLe HFua PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Enabling uncertainty estimation in iterative neural networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692553(12172-12189)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692553
Tan JZhou WLin LJumahong H(2024)A Review of Semantic Medical Image Segmentation Based on Different ParadigmsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34524620:1(1-25)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.4018/IJSWIS.345246
Show More Cited By

Index Terms

UNet++: A Nested U-Net Architecture for Medical Image Segmentation
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Hybrid dilation and attention residual U-Net for medical image segmentation
Abstract
Medical image segmentation is a typical task in medical image processing and critical foundation in medical image analysis. U-Net is well-liked in medical image segmentation, but it doesn't fully explore useful features of the channel ...
Graphical abstract

Display Omitted
Highlights
- A medical image segmentation method is proposed based on U-Net.
- A novel channel ...
GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Abstract
Segmentation of biomedical images is the method of semiautomatic and automatic detection of boundaries within 2D and 3D images. The major challenge of medical image segmentation is the high variability of shape, location, size and texture of the ...
Medical Image Segmentation Using H-minima Transform and Region Merging Technique
FIT '11: Proceedings of the 2011 Frontiers of Information Technology

Watershed transform is a commonly used image segmentation method. The main problem with this segmentation technique is that of its sensitivity to noise and other irregularities which leads to over-segmentation. In this paper the over-segmentation ...

Comments

Information & Contributors

Information

Published In

Sep 2018

397 pages

ISBN:978-3-030-00888-8

DOI:10.1007/978-3-030-00889-5

Editors:
Danail Stoyanov
University College London, London, UK
,
Zeike Taylor
University of Leeds, Leeds, UK
,
Gustavo Carneiro
University of Adelaide, Adelaide, SA, Australia
,
Tanveer Syeda-Mahmood
IBM Research – Almaden, San Jose, CA, USA
,
Anne Martel
Sunnybrook Health Science Centre, Toronto, ON, Canada
,
Lena Maier-Hein
Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
,
João Manuel R.S. Tavares
University of Porto, Porto, Portugal
,
Andrew Bradley
Queensland University of Technology, Brisbane, QLD, Australia
,
João Paulo Papa
Universidade Estadual Paulista, Bauru, São Paulo, Brazil
,
Vasileios Belagiannis
OSRAM (Germany), Garching b. München, Germany
,
Jacinto C. Nascimento
University of Lisbon, Lisboa, Portugal
,
Zhi Lu
ReFUEL4, Singapore, Singapore
,
Sailesh Conjeti
German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
,
Mehdi Moradi
IBM Research – Almaden, San Jose, CA, USA
,
Hayit Greenspan
Tel Aviv University, Tel Aviv, Israel
,
Anant Madabhushi
Case Western Reserve University, Cleveland, OH, USA

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 20 September 2018

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

607
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Brar KGoyal BDogra AMustafa MMajumdar RAlkhayyat AKukreja V(2025)Image segmentation reviewInformation Fusion10.1016/j.inffus.2024.102608114:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.inffus.2024.102608
Durasov NOner DDonier JLe HFua PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Enabling uncertainty estimation in iterative neural networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692553(12172-12189)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692553
Tan JZhou WLin LJumahong H(2024)A Review of Semantic Medical Image Segmentation Based on Different ParadigmsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34524620:1(1-25)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.4018/IJSWIS.345246
Wei ZLiu JYao Y(2024)Semantic-Based Optimization of Deep Learning for Efficient Real-Time Medical Image SegmentationInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34093820:1(1-16)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJSWIS.340938
Li ZAljuaid H(2024)Integrated Design of Building Environment Based on Image Segmentation and Retrieval TechnologyInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.34077417:1(1-14)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJITSA.340774
Yao FJiang XZhao JMa QFan LLiu S(2024)A hybrid model for the clinical manifestation severity assessment of COVID-19 based on Chest CT ImagesProceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science10.1145/3706890.3706937(265-272)Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1145/3706890.3706937
A SPrasad SM MGopal VAhmed.K N(2024)Transformative Techniques in Medical Imaging: Advancing Precision through Computer Vision and Image AnalysisProceedings of the 2024 7th International Conference on Digital Medicine and Image Processing10.1145/3705927.3705946(107-111)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3705927.3705946
Wu JSong TXiao ZZhang FGeng LLiu Y(2024)Fundus image segmentation using SMS-U-Net for the joint delineation of the optic cup and optic discProceedings of the 2024 13th International Conference on Computing and Pattern Recognition10.1145/3704323.3704373(51-58)Online publication date: 25-Oct-2024
https://dl.acm.org/doi/10.1145/3704323.3704373
Guo KTan XZhu XGuo SXi Z(2024)ATMNet: Adaptive Texture Migration Network for Guided Depth Super-ResolutionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370264221:1(1-21)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1145/3702642
Wang YFeng LCai FLi LWu RLi J(2024)TEC-CNN: Toward Efficient Compressing of Convolutional Neural Nets with Low-rank Tensor DecompositionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370264121:2(1-23)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702641
Show More Cited By

Abstract

References

Cited By

Index Terms

Recommendations

Hybrid dilation and attention residual U-Net for medical image segmentation

GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets

Medical Image Segmentation Using H-minima Transform and Region Merging Technique

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations