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PB-FELTuCS: Patch-Based Filtering for Enhanced Liver Tumor Classification and Segmentation

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Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) (MICAD 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1166))

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Abstract

In this paper, we introduce PB-FELTuCS, a hierarchical deep neural network architecture which first performs enhanced 3D liver segmentation and subsequently employs a 3D patch-based filtering algorithm over the 3D liver segments to achieve 3D liver tumor classification and segmentation on LiTS dataset [1]. Despite the simplicity of our liver segmentation network, it surpasses recent benchmarks by achieving an impressive Dice score of 0.98, facilitated by our proposed weighted version of Exponential Logarithmic Dice (ELDice) loss [20]. Furthermore, we propose a filtering approach to extract meaningful 3D patches from the segmented liver, which are then used (as opposed to full volumes) during training of our tumor classification and segmentation networks. This approach enables simpler networks to obtain an accuracy of 89.1% in tumor classification and a 0.747 Dice score for tumor segmentation, highlighting the importance of effective training strategies as an alternative to complex neural network architectures, in enhancing the precision of volumetric medical assessments. Code is available at https://github.com/BheeshmSharma/PBFELTuCS_MICAD2023.

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References

  1. Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 (2019)

  2. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

  3. Chen, Y., Hu, F., Wang, Y., Zheng, C.: Hybrid-attention densely connected U-Net with GAP for extracting livers from CT volumes. Med. Phys. 49(1), 1015–1033 (2022)

    Article  Google Scholar 

  4. Chen, Y., et al.: MS-FANet: multi-scale feature attention network for liver tumor segmentation. Comput. Biol. Med. 163, 107208 (2023)

    Google Scholar 

  5. Chen, Y., et al.: A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans. Comput. Biol. Med. 152, 106421 (2023)

    Google Scholar 

  6. Chi, J., Han, X., Wu, C., Wang, H., Ji, P.: X-net: multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans. Neurocomputing 459(C), 81–96 (2021)

    Google Scholar 

  7. Dickson, J., Linsely, A., Nineta, R.J.A.: An integrated 3D-sparse deep belief network with enriched seagull optimization algorithm for liver segmentation. Multim. Syst. 29(3), 1315–1334 (2023)

    Google Scholar 

  8. Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2020)

    Google Scholar 

  9. Hatamizadeh, A., et al.: Unetr: transformers for 3d medical image segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1748–1758. IEEE Computer Society (2022)

    Google Scholar 

  10. Karimijafarbigloo, S., Azad, R., Kazerouni, A., Merhof, D.: MS-former: multi-scale self-guided transformer for medical image segmentation. In: Med. Imaging Deep Learn. (2023)

    Google Scholar 

  11. Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)

  12. Kushnure, D.T., Tyagi, S., Talbar, S.N.: LiM-net: llightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images. Biomed. Signal Process. Control 80, 104305 (2023)

    Google Scholar 

  13. Lei, T., Wang, R., Zhang, Y., Wan, Y., Liu, C., Nandi, A.K.: DefED-net: deformable encoder-decoder network for liver and liver tumor segmentation. IEEE Trans. Radiat. Plasma Med. Sci. 6, 68–78 (2021)

    Google Scholar 

  14. Liu, H., et al.: GCHA-net: global context and hybrid attention network for automatic liver segmentation. Comput. Biol. Med. 152, 10635 (2023)

    Google Scholar 

  15. Ma, J., Xia, M., Ma, Z., Jiu, Z.: MDAU-Net: a liver and liver tumor segmentation method combining an attention mechanism and multi-scale features. Appl. Sci. 13(18), 10443 (2023)

    Google Scholar 

  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

  17. Song, L., Wang, H., Wang, Z.J.: Bridging the gap between 2D and 3D contexts in CT volume for liver and tumor segmentation. IEEE J. Biomed. Health Inform. 25(9), 3450–3459 (2021)

    Article  Google Scholar 

  18. Wang, C., et al.: Automatic liver segmentation using multi-plane integrated fully convolutional neural networks. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1–6 (2018)

    Google Scholar 

  19. Wang, X., Wang, S., Zhang, Z., Yin, X., Wang, T., Li, N.: CPAD-net: contextual parallel attention and dilated network for liver tumor segmentation. Biomed. Signal Process. Control 79, 104258 (2023). https://doi.org/10.1016/j.bspc.2022.104258

  20. Wong, K.C.L., Moradi, M., Tang, H., Syeda-Mahmood, T.F.: 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. arXiv preprint arXiv:1809.00076 (2018)

  21. Zhou, Z., Siddiquee, R., Mahfuzur, M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA 2018. LNC, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1

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Acknowledgments

We thank Technocraft Centre of Applied Artificial Intelligence (TCA2I), IIT Bombay, for their generous funding support towards this project. We acknowledge Viplove Kanaujia for his help with experiments.

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Correspondence to Bheeshm Sharma .

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Sharma, B., Balamurugan, P. (2024). PB-FELTuCS: Patch-Based Filtering for Enhanced Liver Tumor Classification and Segmentation. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_15

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  • DOI: https://doi.org/10.1007/978-981-97-1335-6_15

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