Skip to main content

Advertisement

SpringerLink
Log in

A cascaded fully convolutional network framework for dilated pancreatic duct segmentation

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Pancreatic duct dilation can be considered an early sign of pancreatic ductal adenocarcinoma (PDAC). However, there is little existing research focused on dilated pancreatic duct segmentation as a potential screening tool for people without PDAC. Dilated pancreatic duct segmentation is difficult due to the lack of readily available labeled data and strong voxel imbalance between the pancreatic duct region and other regions. To overcome these challenges, we propose a two-step approach for dilated pancreatic duct segmentation from abdominal computed tomography (CT) volumes using fully convolutional networks (FCNs).

Methods

Our framework segments the pancreatic duct in a cascaded manner. The pancreatic duct occupies a tiny portion of abdominal CT volumes. Therefore, to concentrate on the pancreas regions, we use a public pancreas dataset to train an FCN to generate an ROI covering the pancreas and use a 3D U-Net-like FCN for coarse pancreas segmentation. To further improve the dilated pancreatic duct segmentation, we deploy a skip connection on each corresponding resolution level and an attention mechanism in the bottleneck layer. Moreover, we introduce a combined loss function based on Dice loss and Focal loss. Random data augmentation is adopted throughout the experiments to improve the generalizability of the model.

Results

We manually created a dilated pancreatic duct dataset with semi-automated annotation tools. Experimental results showed that our proposed framework is practical for dilated pancreatic duct segmentation. The average Dice score and sensitivity were 49.9% and 51.9%, respectively. These results show the potential of our approach as a clinical screening tool.

Conclusions

We investigate an automated framework for dilated pancreatic duct segmentation. The cascade strategy effectively improved the segmentation performance of the pancreatic duct. Our modifications to the FCNs together with random data augmentation and the proposed combined loss function facilitate automated segmentation.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 424–432

  2. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digital Imag 26(6):1045–1057

    Article  Google Scholar 

  3. Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 113–123

  4. Cubuk ED, Zoph B, Shlens J, Le QV (2020) Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 702–703

  5. Edge MD, Hoteit M, Patel AP, Wang X, Baumgarten DA, Cai Q (2007) Clinical significance of main pancreatic duct dilation on computed tomography: single and double duct dilation. World J Gastroenterol WJG 13(11):1701

    Article  Google Scholar 

  6. Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan C (2020) Sa-unet: spatial attention u-net for retinal vessel segmentation. arXiv preprint arXiv:2004.03696

  7. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

  8. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  9. Hojjatoleslami S, Kittler J (1998) Region growing: a new approach. IEEE Trans Image Process 7(7):1079–1084

    Article  CAS  Google Scholar 

  10. 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

  11. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448–456

  12. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International conference for learning representations

  13. Lin TY, 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

  14. 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

  15. Milletari F, Navab N, Ahmadi S (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp 565–571 . https://doi.org/10.1109/3DV.2016.79

  16. Mizrahi JD, Surana R, Valle JW, Shroff RT (2020) Pancreatic cancer. Lancet 395(10242):2008–2020

    Article  CAS  Google Scholar 

  17. Nimura Y, Deguchi D, Kitasaka T, Mori K, Suenaga Y (2008) Pluto: a common platform for computer-aided diagnosis. Med Imag Technol 26(3):187

    Google Scholar 

  18. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999

  19. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in neural information processing systems, pp 8026–8037

  20. 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

  21. Roth H, Oda M, Shimizu N, Oda H, Hayashi Y, Kitasaka T, Fujiwara M, Misawa K, Mori K (2018)Towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks. In: Medical imaging 2018: image processing, International society for optics and photonics, vol 10574, p 105740B

  22. Roth HR, Farag A, Turkbey EB, Lu L, Liu J, Summers RM (2016) Data from pancreas-ct . https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU

  23. Sata N, Kurihara K, Koizumi M, Tsukahara M, Yoshizawa K, Nagai H (2006) Ct virtual pancreatoscopy: a new method for diagnosing intraductal papillary mucinous neoplasm (IPMN) of the pancreas. Abdom Imag 31(3):326

    Article  CAS  Google Scholar 

  24. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69(1):7–34

    Article  Google Scholar 

  25. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition

  26. Tanaka S, Nakaizumi A, Ioka T, Oshikawa O, Uehara H, Nakao M, Yamamoto K, Ishikawa O, Ohigashi H, Kitamra T (2002) Main pancreatic duct dilatation: a sign of high risk for pancreatic cancer. Jpn J Clin Oncol 32(10):407–411

    Article  Google Scholar 

  27. Xia Y, Xie L, Liu F, Zhu Z, Fishman EK, Yuille AL (2018) Bridging the gap between 2d and 3d organ segmentation with volumetric fusion net. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 445–453

  28. Xia Y, Yu Q, Shen W, Zhou Y, Fishman EK, Yuille AL (2020) Detecting pancreatic ductal adenocarcinoma in multi-phase CT scans via alignment ensemble. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 285–295

  29. Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B, Wood BJ, Roth H, Myronenko A, Xu D, Xu Z (2020) Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans Med Imag

  30. Zhou Y, Li Y, Zhang Z, Wang Y, Wang A, Fishman EK, Yuille AL, Park S (2019) Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation. Lect Notes Comput Sci Lect Notes Artif Intell Lect Notes Bioinform 11765:155–163. https://doi.org/10.1007/978-3-030-32245-8_18

    Article  Google Scholar 

  31. Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille, A.L (2017) A fixed-point model for pancreas segmentation in abdominal ct scans. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 693–701

  32. Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL (2019) Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap PT, Khan A (eds) Medical image computing and computer assisted intervention - MICCAI 2019. Springer International Publishing, Cham, pp 3–12

Download references

Acknowledgements

Part of this research was supported by MEXT/JSPS KAKENHI (894030, 17H00867, 21K19898) and the JSPS Bilateral Joint Research Project.

Author information

Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Chen Shen, Yuichiro Hayashi, Masahiro Oda & Kensaku Mori

  2. NVIDIA Corporation, Santa Clara, CA, USA

    Holger R. Roth

  3. Information Strategy Office, Information and Communications, Nagoya University, Nagoya, Japan

    Masahiro Oda

  4. Chiba Kensei Hospital, Chiba, Japan

    Tadaaki Miyamoto & Gen Sato

  5. Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan

    Kensaku Mori

Authors
  1. Chen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Holger R. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuichiro Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masahiro Oda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tadaaki Miyamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gen Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kensaku Mori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kensaku Mori.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the institutional review board of the Chiba Kensei Hospital.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, C., Roth, H.R., Hayashi, Y. et al. A cascaded fully convolutional network framework for dilated pancreatic duct segmentation. Int J CARS 17, 343–354 (2022). https://doi.org/10.1007/s11548-021-02530-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02530-x

Keywords

Search

Navigation