Skip to main content

Advertisement

Springer Nature Link
Log in

Collaborative region-boundary interaction network for medical image segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Existing medical image segmentation methods achieve impressive progress but remain challenged by high diversity in region scales or capricious boundaries. Meanwhile, they usually ignore another favorable factor, i.e., correlations between region and boundary, resulting in limitations to performance improvement. In this paper, we propose a novel framework, aiming at accurate segmentation by collaborating region detection and boundary localization subtasks as well as interacting relations between them. In particular, we first put forward a novel multi-level adaptive feature learning module, which can select discriminative information from backbone features by constructing holistic adaptive weights. Then, a collaborative multi-step refinement module is designed to excavate the reciprocal benefits between two subtasks via reasoning their high correlations cues. Moreover, we propose parallel iterative decoders, i.e., region/boundary iterative decoder, each of which consists of attention-based dual iteration paths to effectively aggregate multi-scale features. By cooperating with these three creative parts, our method sets the new state-of-the-art segmentation performance on five polyp benchmarks, where it achieves a mean Dice score of 92.9% on CVC-ClinicDB dataset. Furthermore, we extensively evaluate our method on both skin lesion segmentation from ISIC 2018 and nuclei segmentation from 2018 Data Science Bowl dataset, demonstrating its excellent generalization ability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

Data sharing is not applicable to this article as no new data were generated or analyzed in this study.

References

  1. An F-P, Liu J- (2021) Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model. Multimed Tools Applic 80(10):15017–15039. https://doi.org/10.1007/s11042-021-10515-w

    Article  Google Scholar 

  2. Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S (2019) Bi-directional convlstm u-net with densley connected convolutions. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp 406–415. https://doi.org/10.1109/ICCVW.2019.00052

  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. https://doi.org/10.1109/TPAMI.2016.2644615

    Article  PubMed  Google Scholar 

  4. Bernal J, Sánchez F J, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111. https://doi.org/10.1016/j.compmedimag.2015.02.007

    Article  PubMed  Google Scholar 

  5. Caicedo J C, Goodman A, Karhohs K W, Cimini B A, Ackerman J, Haghighi M, Heng C, Becker T, Doan M, McQuin C et al (2019) Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nat Methods 16(12):1247–1253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille A L (2018) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848. https://doi.org/10.1109/TPAMI.2017.2699184

    Article  PubMed  Google Scholar 

  7. Chen Z, Zhou H, Lai J, Yang L, Xie X (2021) Contour-aware loss: boundary-aware learning for salient object segmentation. IEEE Trans Image Process 30:431–443. https://doi.org/10.1109/TIP.2020.3037536

    Article  ADS  PubMed  Google Scholar 

  8. Cheng M, Kong Z, Song G, Tian Y, Liang Y, Chen J (2021) Learnable oriented-derivative network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 720–730. https://doi.org/10.1007/978-3-030-87193-2_68

  9. Codella N C F, Gutman D, Celebi M E, Helba B, Marchetti M A, Dusza S W, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), pp 168–172. https://doi.org/10.1109/ISBI.2018.8363547

  10. Dai Y, Gieseke F, Oehmcke S, Wu Y, Barnard K (2021) Attentional feature fusion. In: Proceedings of the IEEE/CVF Winter conference on applications of computer vision, pp 3560–3569. https://doi.org/10.1109/WACV48630.2021.00360

  11. Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: parallel reverse attention network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 263–273. https://doi.org/10.1007/978-3-030-59725-2_26

  12. Fang Y, Chen C, Yuan Y, Tong R K-Y (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 302–310. https://doi.org/10.1007/978-3-030-32239-7_34

  13. Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) Cpfnet: context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39(10):3008–3018. https://doi.org/10.1109/TMI.2020.2983721

    Article  PubMed  Google Scholar 

  14. Fu J, Liu J, Wang Y, Zhou J, Wang C, Lu H (2019) Stacked deconvolutional network for semantic segmentation. IEEE Trans Image Process

  15. Gu R, Wang G, Song T, Huang R, Aertsen M, Deprest J, Ourselin S, Vercauteren T, Zhang S (2020) Ca-net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging 40(2):699–711. https://doi.org/10.1109/TMI.2020.3035253

    Article  Google Scholar 

  16. Guo F, Li W, Kuang Z, Tang J (2021) Mes-net: a new network for retinal image segmentation. Multimed Tools Applic 80(10):14767–14788. https://doi.org/10.1007/s11042-021-10580-1

    Article  Google Scholar 

  17. Jha D, Smedsrud P H, Riegler M A, Halvorsen P, Lange T , Johansen D, Johansen H D (2020) Kvasir-seg: a segmented polyp dataset. In: International conference on multimedia modeling, pp 451–462

  18. Jiang X, Luo Q, Wang Z, Mei T, Wen Y, Li X, Cheng K-T, Yang X (2020) Multi-phase and multi-level selective feature fusion for automated pancreas segmentation from ct images. In: International conference on medical image computing and computer-assisted intervention, pp 460–469. https://doi.org/10.1007/978-3-030-59719-1_45

  19. Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed I B (2021) Boundary loss for highly unbalanced segmentation. Medical Image Anal 67:101851. https://doi.org/10.1016/j.media.2020.101851

    Article  Google Scholar 

  20. Lee H J, Kim J U, Lee S, Kim H G, Ro Y M (2020) Structure boundary preserving segmentation for medical image with ambiguous boundary. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4817–4826. https://doi.org/10.1109/CVPR42600.2020.00487

  21. Li H, Xiong P, Fan H, Sun J (2019) Dfanet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9522–9531. https://doi.org/10.1109/CVPR.2019.00975

  22. Li Y, Zhang Y, Cui W-G, Lei B, Kuang X, Zhang T (2022) Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation. IEEE Trans Med Imaging 41(8):1975–1989. https://doi.org/10.1109/TMI.2022.3151666

    Article  PubMed  Google Scholar 

  23. Liu L, Wu F-X, Wang Y-P, Wang J (2020) Multi-receptive-field CNN for semantic segmentation of medical images. IEEE J Biomed Health Informatics 24(11):3215–3225. https://doi.org/10.1109/JBHI.2020.3016306

    Article  Google Scholar 

  24. Liu X, Yang L, Chen J, Yu S, Li K (2022) Region-to-boundary deep learning model with multi-scale feature fusion for medical image segmentation. Biomed Signal Process Control 71:103165. https://doi.org/10.1016/j.bspc.2021.103165

    Article  Google Scholar 

  25. Liu Y, Zhou J, Liu L, Zhan Z, Hu Y, Fu Y, Duan H (2022) Fcp-net: a feature-compression-pyramid network guided by game-theoretic interactions for medical image segmentation. IEEE Trans Med Imaging 41(6):1482–1496. https://doi.org/10.1109/TMI.2021.3140120

    Article  PubMed  Google Scholar 

  26. 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. https://doi.org/10.1109/CVPR.2015.7298965

  27. Nguyen T-C, Nguyen T-P, Diep G-H, Tran-Dinh A-H, Nguyen T V, Tran M-T (2021) Ccbanet: cascading context and balancing attention for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 633–643. https://doi.org/10.1007/978-3-030-87193-2_60

  28. Oktay O, Schlemper J, Folgoc L L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N Y, Kainz B et al (2018) Attention u-net: learning where to look for the pancreas. arXiv:1804.03999

  29. Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9413–9422. https://doi.org/10.1109/CVPR42600.2020.00943

  30. Patel K, Bur A M, Wang G (2021) Enhanced u-net: a feature enhancement network for polyp segmentation. In: 2021 18th Conference on robots and vision (CRV), pp 181–188. https://doi.org/10.1109/CRV52889.2021.00032

  31. Qin X, Zhang Z, Huang C, Dehghan M, Zaiane O R, Jagersand M (2020) U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn 106:107404. https://doi.org/10.1016/j.patcog.2020.107404

    Article  Google Scholar 

  32. 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, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  33. Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9(2):283–293. https://doi.org/10.1007/s11548-013-0926-3

    Article  PubMed  Google Scholar 

  34. Srivastava A, Jha D, Chanda S, Pal U, Johansen HE D, Johansen D, Riegler M A, Ali S, Halvorsen PE (2022) Msrf-net: a multi-scale residual fusion network for biomedical image segmentation. IEEE J Biomed Health Inform 26(5):2252–2263. https://doi.org/10.1109/JBHI.2021.3138024

    Article  PubMed  Google Scholar 

  35. Sun J, Darbehani F, Zaidi M, Wang B (2020) Saunet: shape attentive u-net for interpretable medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 797–806. https://doi.org/10.1007/978-3-030-59719-1_77

  36. Ta N, Chen H, Lyu Y, Wu T (2022) Ble-net: boundary learning and enhancement network for polyp segmentation. Multimedia Syst, 1–14

  37. Tajbakhsh N, Gurudu S R, Liang J (2016) Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Medical Imaging 35(2):630–644. https://doi.org/10.1109/TMI.2015.2487997

    Article  PubMed  Google Scholar 

  38. Te G, Liu Y, Hu W, Shi H, Mei T (2020) Edge-aware graph representation learning and reasoning for face parsing. In: European conference on computer vision, pp 258–274. https://doi.org/10.1007/978-3-030-58610-2_16

  39. Tomar N K, Jha D, Riegler M A, Johansen H D, Johansen D, Rittscher J, Halvorsen P, Ali S (2022) Fanet: a feedback attention network for improved biomedical image segmentation. IEEE Transactions on Neural Networks and Learning Systems

  40. Vázquez D, Bernal J, Sánchez F J, Fernández-Esparrach G, López A M, Romero A, Drozdzal M, Courville A C (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering

  41. Wang S, Liu M, Lian J, Shen D (2020) Boundary coding representation for organ segmentation in prostate cancer radiotherapy. IEEE Trans Med Imag 40(1):310–320. https://doi.org/10.1109/TMI.2020.3025517

    Article  Google Scholar 

  42. 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 conference on computer vision and pattern recognition, pp 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155

  43. Wang J, Wei L, Wang L, Zhou Q, Zhu L, Qin J (2021) Boundary-aware transformers for skin lesion segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 206–216. https://doi.org/10.1007/978-3-030-87193-2_20

  44. Wang K, Zhang X, Zhang X, Lu Y, Huang S, Yang D (2022) Eanet: iterative edge attention network for medical image segmentation. Pattern Recogn 127:108636. https://doi.org/10.1016/j.patcog.2022.108636

    Article  Google Scholar 

  45. Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi A K (2022) Medical image segmentation using deep learning: a survey. IET Image Process 16 (5):1243–1267. https://doi.org/10.1049/ipr2.12419

    Article  Google Scholar 

  46. Wang R, Chen S, Ji C, Fan J, Li Y (2022) Boundary-aware context neural network for medical image segmentation. Med Image Anal 78:102395. https://doi.org/10.1016/j.media.2022.102395

    Article  PubMed  Google Scholar 

  47. Wang X, Li Z, Huang Y, Jiao Y (2022) Multimodal medical image segmentation using multi-scale context-aware network. Neurocomputing 486:135–146. https://doi.org/10.1016/j.neucom.2021.11.017

    Article  Google Scholar 

  48. Wei J, Wang S, Huang Q (2020) F3net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 12321–12328

  49. Wei J, Wang S, Wu Z, Su C, Huang Q, Tian Q (2020) Label decoupling framework for salient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13025–13034. https://doi.org/10.1109/CVPR42600.2020.01304

  50. Wei J, Hu Y, Zhang R, Li Z, Zhou S K, Cui S (2021) Shallow attention network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 699–708. https://doi.org/10.1007/978-3-030-87193-2_68

  51. Wu H, Chen S, Chen G, Wang W, Lei B, Wen Z (2022) Fat-net: feature adaptive transformers for automated skin lesion segmentation. Med Image Anal 76:102327. https://doi.org/10.1016/j.media.2021.102327

    Article  PubMed  Google Scholar 

  52. Xie S, Girshick R, Dollr P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5987–5995. https://doi.org/10.1109/CVPR.2017.634

  53. Yin Z, Liang K, Ma Z, Guo J (2022) Duplex contextual relation network for polyp segmentation. In: 2022 IEEE 19th international symposium on biomedical imaging (ISBI), pp 1–5. https://doi.org/10.1109/ISBI52829.2022.9761402

  54. Yue G, Han W, Jiang B, Zhou T, Cong R, Wang T (2022) Boundary constraint network with cross layer feature integration for polyp segmentation. IEEE J Biomed Health Inform 26(8):4090–4099. https://doi.org/10.1109/JBHI.2022.3173948

    Article  PubMed  Google Scholar 

  55. Zhang R, Li G, Li Z, Cui S, Qian D, Yu Y (2020) Adaptive context selection for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 253–262. https://doi.org/10.1007/978-3-030-59725-2_25

  56. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 6230–6239. https://doi.org/10.1109/CVPR.2017.660

  57. Zhao C, Shuai R, Ma L, Liu W, Wu M (2022) Segmentation of skin lesions image based on u-net++. Multimed Tools Applic 81(6):8691–8717. https://doi.org/10.1007/s11042-022-12067-z

    Article  Google Scholar 

  58. Zhong J, Wang W, Wu H, Wen Z, Qin J (2020) Polypseg: an efficient context-aware network for polyp segmentation from colonoscopy videos. In: International conference on medical image computing and computer-assisted intervention, pp 285–294. https://doi.org/10.1007/978-3-030-59725-2_28

  59. Zhou Z, Siddiquee M M R, Tajbakhsh N, Liang J (2019) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856–1867. https://doi.org/10.1109/TMI.2019.2959609

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zhou S, Wang J, Wang L, Zhang J, Wang F, Huang D, Zheng N (2020) Hierarchical and interactive refinement network for edge-preserving salient object detection. IEEE Trans Image Process 30:1–14. https://doi.org/10.1109/TIP.2020.3027992

    Article  ADS  MathSciNet  PubMed  Google Scholar 

Download references

Funding

This research is supported by the National Key Research and Development Program of China (2018YFB0804202, 2018YFB0804203), the Regional Joint Fund of NSFC (U19A2057), the National Natural Science Foundation of China (61876070), the Jilin Province Science and Technology Development Plan Project (20190303134SF), and Jilin University Interdisciplinary Integration and Innovation Young Scholars Free Exploration Project (JLUXKJC2021QZ01).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China

    Na Ta, Haipeng Chen, Xue Wang & Zenan Shi

  2. College of Computer, Hulunbuir University, Hulunbuir, 021008, Inner Mongolia, China

    Na Ta

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China

    Na Ta, Haipeng Chen, Xue Wang & Zenan Shi

  4. Department of Cardiology, First Hospital of Jilin University, Changchun, 130021, Jilin, China

    Bing Du

Authors
  1. Na Ta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haipeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zenan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Du.

Ethics declarations

Conflict of Interests

The authors declare that there are no conflicts of interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ta, N., Chen, H., Du, B. et al. Collaborative region-boundary interaction network for medical image segmentation. Multimed Tools Appl 83, 30399–30421 (2024). https://doi.org/10.1007/s11042-023-15505-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15505-8

Keywords