Image segmentation of skin lesions based on dense atrous spatial pyramid pooling and attention mechanism_Journal of Biomedical Engineering

Authors：

YIN Wen ,  ZHOU Dongming , FAN Teng , YU Zhuopu , LI Zhen

School of Information Science and Engineering, Yunnan University, Kunming 650504, P.R. China;

Corresponding author：

ZHOU Dongming, Email: zhoudm@ynu.edu.cn

Keywords：

Skin disease; Image segmentation; Atrous convolution; Attention mechanism; U-Net

DOI：

10.7507/1001-5515.202208015

Video：

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The skin is the largest organ of the human body, and many visceral diseases will be directly reflected on the skin, so it is of great clinical significance to accurately segment the skin lesion images. To address the characteristics of complex color, blurred boundaries, and uneven scale information, a skin lesion image segmentation method based on dense atrous spatial pyramid pooling (DenseASPP) and attention mechanism is proposed. The method is based on the U-shaped network (U-Net). Firstly, a new encoder is redesigned to replace the ordinary convolutional stacking with a large number of residual connections, which can effectively retain key features even after expanding the network depth. Secondly, channel attention is fused with spatial attention, and residual connections are added so that the network can adaptively learn channel and spatial features of images. Finally, the DenseASPP module is introduced and redesigned to expand the perceptual field size and obtain multi-scale feature information. The algorithm proposed in this paper has obtained satisfactory results in the official public dataset of the International Skin Imaging Collaboration (ISIC 2016). The mean Intersection over Union (mIOU), sensitivity (SE), precision (PC), accuracy (ACC), and Dice coefficient (Dice) are 0.901 8, 0.945 9, 0.948 7, 0.968 1, 0.947 3, respectively. The experimental results demonstrate that the method in this paper can improve the segmentation effect of skin lesion images, and is expected to provide an auxiliary diagnosis for professional dermatologists.

Citation： YIN Wen, ZHOU Dongming, FAN Teng, YU Zhuopu, LI Zhen. Image segmentation of skin lesions based on dense atrous spatial pyramid pooling and attention mechanism. Journal of Biomedical Engineering, 2022, 39(6): 1108-1116. doi: 10.7507/1001-5515.202208015 Copy

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7.	Pham D L, Xu C, Prince J L. A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2000, 2(3): 315-337.
8.	Tremeau A, Borel N. A region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191-1203.
9.	Cheng Y. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799.
10.	Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
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15.	Masood A, Al-Jumaily A A. Fuzzy C mean thresholding based level set for automated segmentation of skin lesions. Journal of Signal and Information Processing, 2013, 4(3): 66.
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17.	Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Cham, 2015: 234-241.
18.	Zhou Z, Siddiquee MMR, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support, Springer Cham, 2018, 11045: 3-11.
19.	Xiao X, Lian S, Luo Z, et al. Weighted res-Unet for high-quality retina vessel segmentation//2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2018: 327-331.
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23.	Yuan Y, Chao M, Lo Y C. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE transactions on medical imaging, 2017, 36(9): 1876-1886.
24.	Yuan Y, Lo Y C. Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks. IEEE Journal of Biomedical and Health Informatics, 2017, 23(2): 519-526.
25.	Chen J, Lu Y, Yu Q, et al. Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
26.	Valanarasu J M J, Patel V M. UNeXt: MLP-based rapid medical image segmentation network. arXiv preprint, 2022, arXiv: 2203.04967.
27.	Yang M, Yu K, Zhang C, et al. Denseaspp for semantic segmentation in street scenes//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3684-3692.
28.	李佐勇, 卢妍, 曹新容, 等. 基于双路径和空洞空间金字塔池化的血液白细胞分割. 生物医学工程学杂志, 2022, 39(3): 471-479.
29.	董婷, 魏珑, 叶晓丹, 等. 基于空洞空间卷积池化金字塔结构和注意力机制的全卷积残差网络磨玻璃肺结节分割方法. 生物医学工程学杂志, 2022, 39(3): 441-451.
30.	杨国亮, 邹俊峰, 李世聪, 等. 基于U型稠密特征融合的皮肤病灶分割. 中国医学物理学杂志, 2022, 39(4): 442-447.
31.	Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 3-19.
32.	Gutman D, Codella N C F, Celebi E, et al. Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint, 2016, arXiv: 1605.01397.

1. Siegel R L, Miller K D, Fuchs H E, et al. Cancer statistics, 2022. CA: a Cancer Journal for Clinicians. 2022, 72(1): 7–33.
2. Ge Z, Demyanov S, Chakravorty R, et al. Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Cham, 2017: 250-258.
3. Binder M, Schwarz M, Winkler A, et al. Epiluminescence microscopy: a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives of Dermatology, 1995, 131(3): 286-291.
4. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
5. Pun T. A new method for grey-level picture thresholding using the entropy of the histogram. Signal Processing, 1980, 2(3): 223-237.
6. Yen J C, Chang F J, Chang S. A new criterion for automatic multilevel thresholding. IEEE Transactions on Image Processing, 1995, 4(3): 370-378.
7. Pham D L, Xu C, Prince J L. A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2000, 2(3): 315-337.
8. Tremeau A, Borel N. A region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191-1203.
9. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799.
10. Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
11. Sheikh Y A, Khan E A, Kanade T. Mode-seeking by medoidshifts//2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007: 1-8.
12. Lakshmi S, Sankaranarayanan V. A study of edge detection techniques for segmentation computing approaches. IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” (CASCT), 2010: 35-40.
13. Khan J F, Bhuiyan S M A, Adhami R R. Image segmentation and shape analysis for road-sign detection. IEEE Transactions on Intelligent Transportation Systems, 2010, 12(1): 83-96.
14. Glaister J, Wong A, Clausi D A. Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Transactions on Biomedical Engineering, 2014, 61(4): 1220-1230.
15. Masood A, Al-Jumaily A A. Fuzzy C mean thresholding based level set for automated segmentation of skin lesions. Journal of Signal and Information Processing, 2013, 4(3): 66.
16. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.
17. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Cham, 2015: 234-241.
18. Zhou Z, Siddiquee MMR, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support, Springer Cham, 2018, 11045: 3-11.
19. Xiao X, Lian S, Luo Z, et al. Weighted res-Unet for high-quality retina vessel segmentation//2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2018: 327-331.
20. Alom M Z, Hasan M, Yakopcic C, et al. Recurrent residual convolutional neural network based on U-net (R2U-net) for medical image segmentation. arXiv preprint, 2018, arXiv: 1802.06955.
21. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.
22. Huang H, Lin L, Tong R, et al. Unet 3+: a full-scale connected Unet for medical image segmentation// 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020). IEEE, 2020: 1055-1059.
23. Yuan Y, Chao M, Lo Y C. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE transactions on medical imaging, 2017, 36(9): 1876-1886.
24. Yuan Y, Lo Y C. Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks. IEEE Journal of Biomedical and Health Informatics, 2017, 23(2): 519-526.
25. Chen J, Lu Y, Yu Q, et al. Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
26. Valanarasu J M J, Patel V M. UNeXt: MLP-based rapid medical image segmentation network. arXiv preprint, 2022, arXiv: 2203.04967.
27. Yang M, Yu K, Zhang C, et al. Denseaspp for semantic segmentation in street scenes//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3684-3692.
28. 李佐勇, 卢妍, 曹新容, 等. 基于双路径和空洞空间金字塔池化的血液白细胞分割. 生物医学工程学杂志, 2022, 39(3): 471-479.
29. 董婷, 魏珑, 叶晓丹, 等. 基于空洞空间卷积池化金字塔结构和注意力机制的全卷积残差网络磨玻璃肺结节分割方法. 生物医学工程学杂志, 2022, 39(3): 441-451.
30. 杨国亮, 邹俊峰, 李世聪, 等. 基于U型稠密特征融合的皮肤病灶分割. 中国医学物理学杂志, 2022, 39(4): 442-447.
31. Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 3-19.
32. Gutman D, Codella N C F, Celebi E, et al. Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint, 2016, arXiv: 1605.01397.

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