Abstract
Accurately segmenting the liver into anatomical segments is crucial for surgical planning and lesion monitoring in CT imaging. However, this is a challenging task as it is defined based on vessel structures, and there is no intensity contrast between adjacent segments in CT images. In this paper, we propose a novel point-voxel fusion framework to address this challenge. Specifically, we first segment the liver and vessels from the CT image, and generate 3D liver point clouds and voxel grids embedded with vessel structure prior. Then, we design a multi-scale point-voxel fusion network to capture the anatomical structure and semantic information of the liver and vessels, respectively, while also increasing important data access through vessel structure prior. Finally, the network outputs the classification of Couinaud segments in the continuous liver space, producing a more accurate and smooth 3D Couinaud segmentation mask. Our proposed method outperforms several state-of-the-art methods, both point-based and voxel-based, as demonstrated by our experimental results on two public liver datasets. Code, datasets, and models are released at https://github.com/xukun-zhang/Couinaud-Segmentation.
X. Zhang and Y. Liu—Contributed equally.
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References
Arya, Z., Ridgway, G., Jandor, A., Aljabar, P.: Deep learning-based landmark localisation in the liver for Couinaud segmentation. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) MIUA 2021. LNCS, vol. 12722, pp. 227–237. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80432-9_18
Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)
Bismuth, H.: Surgical anatomy and anatomical surgery of the liver. World J. Surg. 6, 3–9 (1982)
Boltcheva, D., Passat, N., Agnus, V., Jacob-Da Col, M.A., Ronse, C., Soler, L.: Automatic anatomical segmentation of the liver by separation planes. In: Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display, vol. 6141, pp. 383–394. SPIE (2006)
Ching, T., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018)
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.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Couinaud, C.: Liver anatomy: portal (and suprahepatic) or biliary segmentation. Dig. Surg. 16(6), 459–467 (1999)
Huang, S., Wang, B., Cheng, M., Wu, W., Huang, X., Ju, Y.: A fast method to segment the liver according to Couinaud’s classification. In: Gao, X., Müller, H., Loomes, M.J., Comley, R., Luo, S. (eds.) MIMI 2007. LNCS, vol. 4987, pp. 270–276. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79490-5_33
Jia, X., et al.: Boundary-aware dual attention guided liver segment segmentation model. KSII Trans. Internet Inf. Syst. (TIIS) 16(1), 16–37 (2022)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Liu, X., et al.: Secular trend of cancer death and incidence in 29 cancer groups in china, 1990–2017: a joinpoint and age-period-cohort analysis. Cancer Manage. Res. 12, 6221 (2020)
Liu, Z., Tang, H., Lin, Y., Han, S.: Point-voxel CNN for efficient 3d deep learning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Nelson, R., Chezmar, J., Sugarbaker, P., Murray, D., Bernardino, M.: Preoperative localization of focal liver lesions to specific liver segments: utility of CT during arterial portography. Radiology 176(1), 89–94 (1990)
Orcutt, S.T., Anaya, D.A.: Liver resection and surgical strategies for management of primary liver cancer. Cancer Control 25(1), 1073274817744621 (2018)
Pla-Alemany, S., Romero, J.A., Santabárbara, J.M., Aliaga, R., Maceira, A.M., Moratal, D.: Automatic multi-atlas liver segmentation and Couinaud classification from CT volumes. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2826–2829. IEEE (2021)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
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, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)
Soler, L., et al.: Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput. Aided Surg. 6(3), 131–142 (2001)
Soler, L., et al.: 3D image reconstruction for comparison of algorithm database: a patient specific anatomical and medical image database. IRCAD, Strasbourg, France, Technical report, 1(1) (2010)
Tian, J., Liu, L., Shi, Z., Xu, F.: Automatic Couinaud segmentation from CT volumes on liver using GLC-UNet. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 274–282. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_32
Acknowledgement
This project was funded by the National Natural Science Foundation of China (82090052, 82090054, 82001917 and 81930053), Clinical Research Plan of Shanghai Hospital Development Center (No. 2020CR3004A), and National Key Research and Development Program of China under Grant (2021YFC2500402).
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Zhang, X. et al. (2023). Anatomical-Aware Point-Voxel Network for Couinaud Segmentation in Liver CT. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_45
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