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Multimedia Systems
Erschienen in: Multimedia Systems 5/2023

14.07.2023 | Regular Paper

HCPSNet: heterogeneous cross-pseudo-supervision network with confidence evaluation for semi-supervised medical image segmentation

verfasst von: Xianhua Duan, Chaoqiang Jin, Xin Shu

Erschienen in: Multimedia Systems | Ausgabe 5/2023

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Abstract

Medical image segmentation technology can effectively help doctors to diagnose, but there are too little annotated data, which limits the development of fully supervised medical image segmentation methods. This dilemma leads to urgent research on semi-supervised medical image segmentation methods. To cope with this dilemma, we propose a semi-supervised dual flow network, which is called the Heterogeneous Cross-pseudo-supervision Network (HCPSNet). In the HCPSNet, Unet and Swin-Unet are combined for cross-learning, and a shifted patch tokenization (SPT) module is embedded into Swin-Unet to increase the spatial information contained in the feature maps. Besides, a confidence evaluation (CE) module is present to improve the performance of the model. The experimental results on three medical clinical datasets, LA2018, BraTs2019, and ACDC, show that our method can achieve good segmentation results with limited labeled samples. The mean dice of our proposed network on ACDC with seven cases’ samples is 86.17%, about 3% higher than other models.
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Literatur
1.
Hollon, T.C., Pandian, B., Adapa, A.R., et al.: Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26(1), 52–58 (2020)CrossRef
2.
Litjens, G., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRef
3.
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221 (2017)CrossRef
4.
Hesamian, M.H., Jia, W., He, X., et al.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019)CrossRef
5.
Li, X., Chen, H., Qi, X., et al.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)CrossRef
6.
Yang, Z., Xie, L., Zhou, W., et al.: VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation. Multimed. Syst. 4, 1–16 (2022)
7.
Liu, Z., Han, K., Wang, Z., et al.: Automatic liver segmentation from abdominal CT volumes using improved convolution neural networks. Multimed. Syst. 27(1), 111–124 (2021)CrossRef
8.
Bai, W., Suzuki, H., Huang, J., et al.: A population-based phenome-wide association study of cardiac and aortic structure and function. Nat. Med. 26(10), 1654–1662 (2020)CrossRef
9.
Mei, X., Lee, H.C., Diao, K., et al.: Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat. Med. 26(8), 1224–1228 (2020)CrossRef
10.
Olimov, B., Sanjar, K., Din, S., et al.: FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. Multimed. Syst. 27(4), 637–650 (2021)CrossRef
11.
Peixoto, S.A., Medeiros, A.G., Hassan, M.M., et al.: Floor of log: a novel intelligent algorithm for 3D lung segmentation in computer tomography images. Multimed. Syst. 7, 1–13 (2020)
12.
Olya, M.H., Badri, H., Teimoori, S., et al.: An integrated deep learning and stochastic optimization approach for resource management in team-based healthcare systems. Exp. Syst. Appl. 187, 115924 (2022)CrossRef
13.
Li, Z., Zhao, S., Chen, Y., et al.: A deep-learning-based framework for severity assessment of COVID-19 with CT images. Exp. Syst. Appl. 185, 115616 (2021)CrossRef
14.
Zhang, H., Zhang, W., Shen, W., et al.: Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution. Biomed. Signal Process. Control 68, 102684 (2021)CrossRef
15.
Wang, W., Wang, Y., Wu, Y., et al.: Quantification of full left ventricular metrics via deep regression learning with contour-guidance. IEEE Access 7, 47918–47928 (2019)CrossRef
16.
Shu, X., Chang, F., Zhang, X., et al.: ECAU-Net: efficient channel attention U-Net for fetal ultrasound cerebellum segmentation. Biomed. Signal Process. Control 75, 103528 (2022)CrossRef
17.
Shu, X., Gu, Y., Zhang, X., et al.: FCRB U-net: a novel fully connected residual block U-net for fetal cerebellum ultrasound image segmentation. Comput. Biol. Med. 3, 105693 (2022)CrossRef
18.
Kiran, I., Raza, B., Ijaz, A., et al.: DenseRes-Unet: segmentation of overlapped/clustered nuclei from multi organ histopathology images. Comput. Biol. Med. 143, 105267 (2022)CrossRef
19.
Pi, J., Qi, Y., Lou, M., et al.: FS-UNet: mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening. Comput. Biol. Med. 137, 104800 (2021)CrossRef
20.
Wang, Y., Huang, L., Wu, M., et al.: Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays. Comput. Biol. Med. 146, 105576 (2022)CrossRef
21.
Xiong, Z., Xia, Q., Hu, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)CrossRef
22.
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30, 112 (2017)
23.
Yu, L., Wang, S., Li, X., et al.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 605–613. Springer, Cham (2019).
24.
Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 552–561. Springer, Cham (2020).
25.
Luo, X., Chen, J., Song, T. et al.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35(10), pp. 8801–8809 (2021).
26.
Qiao, S., Shen, W., Zhang, Z. et al.: Deep co-training for semi-supervised image recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 135–152 (2018).
27.
Zhang, Y., Yang, L., Chen, J. et al.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 408–416. Springer, Cham (2017).
28.
Vu, T.H., Jain, H., Bucher, M. et al.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517–2526 (2019).
29.
Peng, J., Wang, P., Desrosiers, C., et al.: Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels. Adv. Neural. Inf. Process. Syst. 34, 16686–16699 (2021)
30.
Ke, Z., Qiu, D., Li, K. et al.: Guided collaborative training for pixel-wise semi-supervised learning. In: European Conference on Computer Vision, pp. 429–445. Springer, Cham (2020).
31.
Chen, X., Yuan, Y., Zeng, G. et al.: Semi-supervised semantic segmentation with cross pseudo-supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2622 (2021).
32.
Peiris, H., Chen, Z., Egan, G. et al.: Duo-SegNet: adversarial dual-views for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 428–438. Springer, Cham (2021).
33.
Li, C., Liu, H.: Generative adversarial semi-supervised network for medical image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 303–306. (IEEE, 2021).
34.
Bortsova, G., Dubost, F., Hogeweg, L. et al.: Semi-supervised medical image segmentation via learning consistency under transformations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 810–818. Springer, Cham (2019).
35.
Hang, W., Feng, W., Liang, S. et al.: Local and global structure-aware entropy regularized mean teacher model for 3D left atrium segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 562–571. Springer, Cham (2020).
36.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015).
37.
Dosovitskiy, A., Beyer, L., Kolesnikov, A. et al.: An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv preprint: arXiv:​2010.​11929 (2020).
38.
Touvron, H., Cord, M., Douze, M. et al.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning. PMLR, pp. 10347–10357 (2021).
39.
Liu, Z., Lin, Y., Cao, Y. et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021).
40.
Chen, J., Lu, Y., Yu, Q. et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint: arXiv:​2102.​04306 (2021).
41.
Cao, H., Wang, Y., Chen, J. et al.: Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:​2105.​05537 (2021).
42.
Luo, X., Hu, M., Song, T. et al.: Semi-supervised medical image segmentation via cross teaching between CNN and transformer. arXiv preprint: arXiv:​2112.​04894 (2021).
43.
44.
Touvron, H., Cord, M., El-Nouby, A. et al.: Three things everyone should know about vision transformers. arXiv preprint: arXiv:​2203.​09795 (2022).
45.
Li, X., Luo, G., Wang, K.: Multi-Step Cascaded Networks for Brain Tumor Segmentation. International MICCAI Brainlesion Workshop, pp. 163–173. Springer, Cham (2019)
46.
Luo, X., Liao, W., Chen, J. et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 318–329. Springer, Cham (2021).
Metadaten
Titel
HCPSNet: heterogeneous cross-pseudo-supervision network with confidence evaluation for semi-supervised medical image segmentation
verfasst von
Xianhua Duan
Chaoqiang Jin
Xin Shu
Publikationsdatum
14.07.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 5/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01135-5

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