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DmADs-Net: dense multiscale attention and depth-supervised network for medical image segmentation

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Abstract

Deep learning has made important contributions to the development of medical image segmentation. Convolutional neural networks, as a crucial branch, have attracted strong attention from researchers. Through the tireless efforts of numerous researchers, convolutional neural networks have yielded numerous outstanding algorithms for processing medical images. The ideas and architectures of these algorithms have also provided important inspiration for the development of later technologies.Through extensive experimentation, we have found that currently mainstream deep learning algorithms are not always able to achieve ideal results when processing complex datasets and different types of datasets. These networks still have room for improvement in lesion localization and feature extraction. Therefore, we have created the dense multiscale attention and depth-supervised network (DmADs-Net).We use ResNet for feature extraction at different depths and create a Multi-scale Convolutional Feature Attention Block to improve the network’s attention to weak feature information. The Local Feature Attention Block is created to enable enhanced local feature attention for high-level semantic information. In addition, in the feature fusion phase, a Feature Refinement and Fusion Block is created to enhance the fusion of different semantic information.We validated the performance of the network using five datasets of varying sizes and types. Results from comparative experiments show that DmADs-Net outperformed mainstream networks. Ablation experiments further demonstrated the effectiveness of the created modules and the rationality of the network architecture.

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Data available on request from the authors.

References

  1. Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2019) Dataset of breast ultrasound images. Data Brief 28:104863

    Article  Google Scholar 

  2. 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

  3. Badrinarayanan V, Kendall A, Cipolla R (2017) SEGNet: a deep convolutional encoder–decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  4. Chaitanya K, Erdil E, Karani N, Konukoglu E (2020) Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv Neural Inf Process Syst 33:12546–12558

    Google Scholar 

  5. Chen H, Gu J, Zhang Z (2021) Attention in attention network for image super-resolution. ar**v preprint ar**v:2104.09497

  6. Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: Transformers make strong encoders for medical image segmentation. ar**v preprint ar**v:2102.04306

  7. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  8. Chen Y, Dai X, Liu M, Chen D, Yuan L, Liu Z (2020) Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11030–11039

  9. Darshik A, Dev A, Bharath M, Nair BA, Gopakumar G (2020) Semantic segmentation of spectral images: a comparative study using FCN8s and U-Net on RIT-18 dataset. In: 2020 11th international conference on computing, communication and networking technologies (ICCCNT). IEEE, pp 1–6

  10. Ding Q, Shao Z, Huang X, Altan O (2021) DSA-Net: a novel deeply supervised attention-guided network for building change detection in high-resolution remote sensing images. Int J Appl Earth Obs Geoinf 105:102591

    Google Scholar 

  11. Dong Z, Li J, Hua Z (2024) Transformer-based multi-attention hybrid networks for skin lesion segmentation. Expert Syst Appl 244:123016

    Article  Google Scholar 

  12. Fu B, Peng Y, He J, Tian C, Sun X, Wang R (2024) HMSU-Net: a hybrid multi-scale u-net based on a CNN and transformer for medical image segmentation. Comput Biol Med 170:108013

    Article  Google Scholar 

  13. Gutman D, Codella NC, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) 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). ar**s in deep residual networks. In: Computer Vision–ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer, pp 630–645

  14. 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

  15. Hu X, Zeng D, Xu X, Shi Y (2021) Semi-supervised contrastive learning for label-efficient medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 481–490

  16. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  17. Ibtehaz N, Rahman MS (2020) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74–87

    Article  Google Scholar 

  18. Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P, Johansen HD (2019) ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE international symposium on multimedia (ISM). IEEE, pp 225–2255

  19. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  20. Lee CY, **e S, Gallagher P, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: Artificial intelligence and statistics. PMLR, pp 562–570

  21. Li R, Zheng S, Zhang C, Duan C, Su J, Wang L, Atkinson PM (2021) Multiattention network for semantic segmentation of fine-resolution remote sensing images. IEEE Trans Geosci Remote Sens 60:1–13

    Google Scholar 

  22. Li S, Liu J, Song Z (2022) Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net. Int J Mach Learn Cybern 13(9):2435–2445

    Article  Google Scholar 

  23. Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674

    Article  Google Scholar 

  24. Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 510–519

  25. Li Y, Yang H, Wang J, Zhang C, Liu Z, Chen H (2022) An image fusion method based on special residual network and efficient channel attention. Electronics 11(19):3140

    Article  Google Scholar 

  26. Liu J, Zhang W, Tang Y, Tang J, Wu G (2020) Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2359–2368

  27. Liu L, Huang Z, Liò P, Schönlieb CB, Aviles-Rivero AI (2022) PC-SwinMorph: patch representation for unsupervised medical image registration and segmentation. ar**v preprint ar**v:2203.05684

  28. Liu X, Wang Z, Wan J, Zhang J, ** Y, Liu R, Miao Q (2023) RoadFormer: road extraction using a swin transformer combined with a spatial and channel separable convolution. Remote Sens 15(4):1049

    Article  Google Scholar 

  29. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022

  30. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  31. Makandar A, Halalli B (2016) Threshold based segmentation technique for mass detection in mammography. J Comput 11(6):472–478

    Article  Google Scholar 

  32. 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

  33. Qu H, Riedlinger G, Wu P, Huang Q, Yi J, De S, Metaxas D (2019) Joint segmentation and fine-grained classification of nuclei in histopathology images. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, pp 900–904

  34. Rajalakshmi NR, Vidhyapriya R, Elango N, Ramesh N (2021) Deeply supervised U-Net for mass segmentation in digital mammograms. Int J Imaging Syst Technol 31(1):59–71

    Article  Google Scholar 

  35. 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. Springer, pp 234–241

  36. Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Ki Komatsu, Matsui M, Fujita H, Kodera Y, Doi K (2000) Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol 174(1):71–74

    Article  Google Scholar 

  37. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v preprint ar**v:1409.1556

  38. Sirinukunwattana K, Pluim JP, Chen H, Qi X, Heng PA, Guo YB, Wang LY, Matuszewski BJ, Bruni E, Sanchez U et al (2017) Gland segmentation in colon histology images: the GLaS challenge contest. Med Image Anal 35:489–502

    Article  Google Scholar 

  39. Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. PMLR, pp 2256–2265

  40. Sridevi M, Mala C (2012) A survey on monochrome image segmentation methods. Procedia Technol 6:548–555

    Article  Google Scholar 

  41. Vahadane A, Atheeth B, Majumdar S (2021) Dual encoder attention u-net for nuclei segmentation. In: 2021 43rd annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp 3205–3208

  42. Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM (2021) Medical transformer: gated axial-attention for medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 36–46

  43. Valanarasu JMJ, Sindagi VA, Hacihaliloglu I, Patel VM (2021) KIU-Net: overcomplete convolutional architectures for biomedical image and volumetric segmentation. IEEE Trans Med Imaging 41(4):965–976

    Article  Google Scholar 

  44. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30

  45. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  46. Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803

  47. Wu K, Zhang D (2015) Robust tongue segmentation by fusing region-based and edge-based approaches. Expert Syst Appl 42(21):8027–8038

    Article  Google Scholar 

  48. Xu Q, Duan W, He N (2022) DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation. ar**v preprint ar**v:2202.00972

  49. Yang Y, Wan W, Huang S, Zhong X, Kong X (2023) RADCU-Net: residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation. Int J Mach Learn Cybern 14:1605–1620

  50. Zagoruyko S, Komodakis N (2016) Wide residual networks. ar**v preprint ar**v:1605.07146

  51. Zhan B, Song E, Liu H, Gong Z, Ma G, Hung CC (2023) CFNet: a medical image segmentation method using the multi-view attention mechanism and adaptive fusion strategy. Biomed Signal Process Control 79:104112

    Article  Google Scholar 

  52. Zhao X, Zhang L, Lu H (2021) Automatic polyp segmentation via multi-scale subtraction network. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 120–130

  53. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 3–11

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant nos. 61772319, 62002200, 62202268, 62272281), Shandong Provincial Science and Technology Support Program of Youth Innovation Team in Colleges (under Grant 2021KJ069, 2019KJN042), Yantai science and technology innovation development plan (2022JCYJ031).

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  1. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China

    Zhao** Fu, **jiang Li, Zheng Chen & Lu Ren

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Fu, Z., Li, J., Chen, Z. et al. DmADs-Net: dense multiscale attention and depth-supervised network for medical image segmentation. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02248-7

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