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Improved U-Net based on contour attention for efficient segmentation of skin lesion

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Abstract

In the field of skin disease diagnosis, the segmentation of lesions is a crucial step in auxiliary diagnosis. The U-Net network has achieved many successes in the field of medical segmentation. However, due to the relatively simple encoder and decoder of traditional U-Net, it is easy to be affected by factors such as small sample size, insufficient feature expression, and irregular shape of segmented targets, the good features cannot be extracted, resulting in incorrect segmentation results. In order to solve these problems and improve the performance of lesion segmentation. We designed a new U-Net network by improving the traditional architecture in two ways: 1) We replaced the encoder with a robust classification network to effectively extract feature information. 2) To thoroughly exploit the potential perceptual clues in the multi-scale feature maps generated by the encoder, we designed the Multi-Scale Depth Feature Exploration (MSD) module as the decoder. Additionally, we propose a novel segmentation loss function named Weighted Contour Cross Entropy loss (WCCE), which forces the network pay more attettion to lesion contour information and improve the contour quality of lesion segmentation to achieve better segmentation accuracy. All experiments were conducted on two skin lesion datasets, ISIC2018 and XJUSL. Experimental analysis reveals that the improved U-Net assisted with proposed loss surpasses the traditional U-Net and other typical methods in the performance of all evaluation metrics used in this study. This result reflects that the proposed method can effectively segment the lesion contour and improve the performance of lesion segmentation.

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Availability of data and materials

The ISIC 2018 dataset available at https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000?select=HAM10000_images_part_1. But the XJUSL dataset used to support the findings of this study are available from the corresponding author upon request.

References

  1. Yong L, Yu Y, Li B, Ge H, Zhen Q, Mao Y, Yu Y, Cao L, Zhang R, Li Z et al (2022) Calcium/calmodulin-dependent protein kinase iv promotes imiquimod-induced psoriatic inammation via macrophages and keratinocytes in mice. Nature Communications 13(1):4255

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Elashiri MA, Rajesh A, Pandey SN, Shukla SK, Urooj S et al (2022) Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory. Biomedical Signal Processing and Control 76:103729

    Article  Google Scholar 

  3. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132–7141

  4. Han Q, Wang H, Hou M, Weng T, Pei Y, Li Z, Chen G, Tian Y, Qiu Z (2023) Hwa-segnet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment. Computers in Biology and Medicine 152:106343

    Article  PubMed  Google Scholar 

  5. Li D, Chu X, Cui Y, Zhao J, Zhang K, Yang X (2022) Improved u-net based on contour prediction for efficient segmentation of rectal cancer. Computer Methods and Programs in Biomedicine 213:106493

    Article  PubMed  Google Scholar 

  6. Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed IB (2021) Boundary loss for highly unbalanced segmentation. Medical image analysis 67:101851

    Article  PubMed  Google Scholar 

  7. Bokhovkin A, Burnaev E (2019) Boundary loss for remote sensing imagery semantic segmentation. In: Advances in Neural Networks–ISNN 2019: 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part II 16, pp 388–401. Springer

  8. Duan J, Bernard ME, Castle JR, Feng X, Wang C, Kenamond MC, Chen Q (2023) Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation. Med Phys

  9. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation

  10. Long J, Shelhamer E, Darrell T (2015) Fully Convolutional Networks for Semantic Segmentation

  11. Dong C, Dai D, Zhang Y, Zhang C, Li Z, Xu S (2023) Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification. Comput Biol Med 152:106321

    Article  PubMed  Google Scholar 

  12. Zunair H, Hamza AB (2021) Sharp u-net: Depthwise convolutional network for biomedical image segmentation. Comput Biol Med 136:104699

    Article  PubMed  Google Scholar 

  13. Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp 565–571. IEEE

  14. Zhang B, Ma L, Zhao H, Hao Y, Fu S, Wang H, Li Y, Han H (2022) Automatic segmentation of hyperreflective dots via focal priors and visual saliency. Med Phys 49(11):7025–7037

    Article  PubMed  Google Scholar 

  15. Csurka G, Larlus D, Perronnin F, Meylan F (2013) What is a good evaluation measure for semantic segmentation?. In: BMVC, vol. 27, pp 10–5244. Bristol

  16. He K, Zhang X, Ren S, Sun J (2015) Deep Residual Learning for Image Recognition

  17. Liang S, Tian S, Kang X, Zhang D, Wu W, Yu L (2023) Skin lesion classification base on multi-hierarchy contrastive learning with pareto optimality. Biomed Signal Process Control 86:105187

    Article  Google Scholar 

  18. Yang X, Fan J, Wu C, Zhou D, Li T (2022) Nasmamsr: a fast image super-resolution network based on neural architecture search and multiple attention mechanism. Multimed Syst, 1–14

  19. Dong C, Dai D, Zhang Y, Zhang C, Li Z, Xu S (2023) Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification. Comput Biol Med 152:106321

    Article  PubMed  Google Scholar 

  20. Wang W, Zhou T, Yu F, Dai J, Konukoglu E, Van Gool L (2021) Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp7303–7313

  21. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980–2988

  22. Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). ar**v preprint ar**v:1902.03368

  23. Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5(1):1–9

    Article  Google Scholar 

  24. Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. ar**v preprint ar**v:1802.06955

  25. Oktay O, Schlemper J, Folgoc LL., Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et al (2018) Attention u-net: Learning where to look for the pancreas. ar**v preprint ar**v:1804.03999

  26. Chen, L-C, Zhu Y, Papandreou G, Schro F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818

  27. Valanarasu JMJ, Patel VM (2022) Unext: Mlp-based rapid medical image segmentation network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 23–33. Springer

  28. Verma R, Kumar N, Patil A, Kurian NC, Rane S, Graham S, Vu QD, Zwager M, Raza SEA, Rajpoot N, others (2021) MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge. IEEE Transactions on Medical Imaging 40:3413–3423

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Acknowledgements

The authors thank the research assistances and all staff of **njiang Uygur Autonomous Region Key R\( \& \)D program and National Natural Science Foundation of China.

Funding

We acknowledge the support of **njiang Uygur Autonomous Region Key R\( \& \)D program under Grant 2021B03001-4 and National Natural Science Foundation of China under Grant 62362061.

Author information

Authors and Affiliations

  1. Collage of Software, **njiang University, Urumuqi, 830000, China

    Shuang Liang & Shengwei Tian

  2. Key Laboratory of Software Engineering Technology, **njiang University, Urumuqi, 830000, China

    Shuang Liang & Shengwei Tian

  3. College of Network Center, **njiang University, Urumqi, 830000, China

    Long Yu

  4. College of Information Science and Engineering, **njiang University, Urumqi, 830000, China

    Long Yu

  5. Department of Dermatology and Venereology, People’s Hospital of **njiang Uygur AutonomousRegion, Urumqi, 830000, China

    **ao**g Kang

  6. **njiang Clinical Research Center For Dermatologic Diseases, Urumqi, 830000, China

    **ao**g Kang

  7. **njiang Key Laboratory of Dermatology Research(XJYS1707), Urumqi, 830000, China

    **ao**g Kang

Authors
  1. Shuang Liang
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  2. Shengwei Tian
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  3. Long Yu
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Contributions

Shuang Liang: Writing of original draft, methodology, and software. Shengwei Tian: Review, editing, and data curation. Long Yu: Investigation and methodology. **ao**g Kang: Data annotation and pathological consultation. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shengwei Tian or **ao**g Kang.

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Liang, S., Tian, S., Yu, L. et al. Improved U-Net based on contour attention for efficient segmentation of skin lesion. Multimed Tools Appl 83, 33371–33391 (2024). https://doi.org/10.1007/s11042-023-16759-y

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