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FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

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Advances in Soft Computing (MICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14392))

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Abstract

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.

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References

  1. Aldoj, N., Biavati, F., Michallek, F., Stober, S., Dewey, M.: Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. Sci. Rep. 10 (2020). https://doi.org/10.1038/s41598-020-71080-0

  2. Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on U-net (R2U-net) for medical image segmentation. CoRR abs/1802.06955 (2018)

    Google Scholar 

  3. AstraZeneca: A personalized approach in prostate cancer (2020). https://www.astrazeneca.com/our-therapy-areas/oncology/prostate-cancer.html. Accessed 17 Oct 2021

  4. Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_9

    Chapter  Google Scholar 

  5. Chen, M., et al.: Prostate cancer detection: comparison of T2-weighted imaging, diffusion-weighted imaging, proton magnetic resonance spectroscopic imaging, and the three techniques combined. Acta Radiologica 49(5), 602–610 (2008). https://doi.org/10.1080/02841850802004983. https://www.tandfonline.com/doi/abs/10.1080/02841850802004983

  6. Elguindi, S., et al.: Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Phys. Imaging Radiation Oncol. 12, 80–86 (2019). https://doi.org/10.1016/j.phro.2019.11.006. https://www.sciencedirect.com/science/article/pii/S2405631619300569

  7. Haralick, R., Shapiro, L.: Image segmentation techniques. Comput. Vis. Graph. Image Process. 29(1), 100–132 (1985). https://doi.org/10.1016/S0734-189X(85)90153-7. https://www.sciencedirect.com/science/article/pii/S0734189X85901537

  8. Li, S., Dong, M., Du, G., Mu, X.: Attention Dense-U-Net for automatic breast mass segmentation in digital mammogram. IEEE Access 7, 59037–59047 (2019). https://doi.org/10.1109/ACCESS.2019.2914873

    Article  Google Scholar 

  9. Liu, Y., et al.: Automatic prostate zonal segmentation using fully convolutional network with feature pyramid attention. IEEE Access 7, 163626–163632 (2019). https://doi.org/10.1109/ACCESS.2019.2952534

    Article  Google Scholar 

  10. Liu, Y., et al.: Exploring uncertainty measures in Bayesian deep attentive neural networks for prostate zonal segmentation. IEEE Access 8, 151817–151828 (2020). https://doi.org/10.1109/ACCESS.2020.3017168

    Article  Google Scholar 

  11. Mahapatra, D., Buhmann, J.M.: Prostate MRI segmentation using learned semantic knowledge and graph cuts. IEEE Trans. Biomed. Eng. 61(3), 756–764 (2014). https://doi.org/10.1109/TBME.2013.2289306

    Article  Google Scholar 

  12. Mata, C., Munuera, J., Lalande, A., Ochoa-Ruiz, G., Benitez, R.: MedicalSeg: a medical GUI application for image segmentation management. Algorithms 15(06) (2022). https://doi.org/10.3390/a15060200. https://www.mdpi.com/1999-4893/15/6/200

  13. Oktay, O., et al.: Attention U-net: learning where to look for the pancreas (2018). https://doi.org/10.48550/ARXIV.1804.03999

  14. Rasch, C., et al.: Human-computer interaction in radiotherapy target volume delineation: a prospective, multi-institutional comparison of user input devices. J. Digit. Imaging 24(5), 794–803 (2011). https://doi.org/10.1007/s10278-010-9341-2

    Article  Google Scholar 

  15. Rodríguez, J., Ochoa-Ruiz, G., Mata, C.: A prostate MRI segmentation tool based on active contour models using a gradient vector flow. Appl. Sci. 10(18) (2020). https://doi.org/10.3390/app10186163. https://www.mdpi.com/2076-3417/10/18/6163

  16. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  17. Wu, Y., Wu, J., Jin, S., Cao, L., Jin, G.: Dense-U-Net: dense encoder-decoder network for holographic imaging of 3D particle fields. Opt. Commun. 493, 126970 (2021). https://doi.org/10.1016/j.optcom.2021.126970. https://www.sciencedirect.com/science/article/pii/S0030401821002200

  18. Zhu, Q., Du, B., Turkbey, B., Choyke, P.L., Yan, P.: Deeply-supervised CNN for prostate segmentation. CoRR abs/1703.07523 (2017). http://arxiv.org/abs/1703.07523

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Acknowledgments

The authors wish to acknowledge the Mexican Council for Science and Technology (CONAHCYT) for the support in terms of postgraduate scholarships in this project, and the Data Science Hub at Tecnologico de Monterrey for their support on this project.

This work has been supported by Azure Sponsorship credits granted by Microsoft’s AI for Good Research Lab through the AI for Health program. The project was also supported by the French-Mexican ANUIES CONAHCYT Ecos Nord grant 322537.

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Authors and Affiliations

  1. School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico

    Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza & Gilberto Ochoa-Ruiz

  2. Universitat Politècnica de Catalunya, 08019, Barcelona, Catalonia, Spain

    Christian Mata

  3. Pediatric Computational Imaging Research Group, Hospital Sant Joan de Déu, 08950, Esplugues de Llobregat, Catalonia, Spain

    Christian Mata

Authors
  1. Pablo Cesar Quihui-Rubio
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  2. Daniel Flores-Araiza
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  3. Miguel Gonzalez-Mendoza
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  4. Christian Mata
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  5. Gilberto Ochoa-Ruiz
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Corresponding author

Correspondence to Gilberto Ochoa-Ruiz .

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Editors and Affiliations

  1. Center for Computing Research, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, Mexico

    Hiram Calvo

  2. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Hiram Ponce

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Quihui-Rubio, P.C., Flores-Araiza, D., Gonzalez-Mendoza, M., Mata, C., Ochoa-Ruiz, G. (2024). FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Soft Computing. MICAI 2023. Lecture Notes in Computer Science(), vol 14392. Springer, Cham. https://doi.org/10.1007/978-3-031-47640-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-47640-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47639-6

  • Online ISBN: 978-3-031-47640-2

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