Main Article Content
Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. The source code of this study is publicly available at https://github.com/kennyha85/Liver-segmentation.
Keywords: Liver segmentations, CNNs, Connected Components, Post processing
 K.A. McGlynn, J.L. Petrick, W.T. London, Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clinics in liver disease 19(2) (2015) 223-238.
 M. Mohammadian, N. Mahdavifar, A. Mohammadian-Hafshejani, H. Salehiniya, Liver cancer in the world: epidemiology, incidence, mortality and risk factors, World Cancer Res J. 5(2) (2018) e1082.
 T.T. Hong, N. Phuong Hoa, S.M. Walker, P.S. Hill, C. Rao, Completeness and reliability of mortality data in Viet Nam: Implications for the national routine health management information system, PloS one 13(1) 2018) e0190755. https://doi.org/10.1371/journal.pone.0190755.
 T. Pham, L. Bui, G. Kim, D. Hoang, T. Tran, M. Hoang, Cancers in Vietnam-Burden and Control Efforts: A Narrative Scoping Review. Cancer Control 26(1) (2019) 1073274819863802.
 M. Borner, M. Castiglione, J. Triller, H.U. Baer, M. Soucek, L. Blumgart, K. Brunner, Arena: Considerable side effects of chemoembolization for colorectal carcinoma metastatic to the liver, Annals of oncology 3(2) (1992) 113-115.
 K. Memon, R.J. Lewandowski, L. Kulik, A. Riaz, M.F. Mulcahy, R. Salem, Radioembolization for primary and metastatic liver cancer, In Seminars in radiation oncology, WB Saunders. 21(4) (2011) 294-302.
 I. Gory, M. Fink, S. Bell, P. Gow, A. Nicoll, V. Knight, W. Kemp, Radiofrequency ablation versus resection for the treatment of early stage hepatocellular carcinoma: A multicenter Australian study, Scandinavian journal of gastroenterology 50(5) (2015) 567-576.
 H.M. Luu, C. Klink, W. Niessen, A. Moelker, T. Van Walsum, Non-rigid registration of liver CT images for CT-guided ablation of liver tumors. PloS one, 11(9) 92016) e0161600.
 G. Gunay, M.H. Luu, A. Moelker, T. Van Walsum, S. Klein, Semiautomated registration of pre‐and intraoperative CT for image‐guided percutaneous liver tumor ablation interventions, Medical physics 44(7) (2017) 3718-3725.
 A. Gotra, L. Sivakumaran, G. Chartrand, N. Vu, F. Vandenbroucke-Menu, C. Kauffmann, A. Tang, Liver segmentation: Indications, techniques and future directions, Insights into imaging 8(4) (2017) 377-392. https://doi.org/10.1007/s13244-017-0558-1.
 T. Heimann, B. Van Ginneken, M.A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, F. Bello, Comparison and evaluation of methods for liver segmentation from CT datasets, IEEE transactions on medical imaging 28(8) (2009) 1251-1265.
 O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, 2015, pp. 234-241.
 F. Milletari, N. Navab, S.A. Ahmadi, October, V-net: Fully convolutional neural networks for volumetric medical image segmentation, In 2016 Fourth International Conference on 3D Vision (3DV) IEEE, 2016, pp. 565-571.
 P.F. Christ, F. Ettlinger, F. Grün, M.A. Elshaera, J. Lipkova, S. Schlecht, M. Rempfler, Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks, arXiv preprint arXiv:1702.05970, 2017.
 P.F. Christ, M.E.A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, H. Sommer, Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2016, pp. 415-423.
 H. Meine, G. Chlebus, M. Ghafoorian, I. Endo, A. Schenk, Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. arXiv preprint arXiv, 2018, pp. 1810.04017.
 X. Li, H. Chen, X. Qi, Q. Dou, C.W. Fu, P.A. Heng, H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes, IEEE transactions on medical imaging, 37(12) (2018) 2663-2674.
 M. Bellver, K.K. Maninis, J. Pont-Tuset, X. Giró-i-Nieto, J. Torres, L. Van Gool, Detection-aided liver lesion segmentation using deep learning, ArXiv preprint arXiv:1711.11069, 2017.
 H.S. Hoang, C.P. Pham, D. Franklin, T. Van Walsum, M.H. Luu, An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers, In 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, September, 2019, pp. 20-25.
 H. Samet, M. Tamminen, Efficient component labeling of images of arbitrary dimension represented by linear bintrees, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4) (1988) 579-586.
 P. Bilic, P.F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, S. Kadoury, The liver tumor segmentation benchmark (lits), ArXiv preprint arXiv, 2019, 1901.04056.
 H.M. Luu, A. Moelker, S. Klein, W. Niessen, T. Van Walsum, Quantification of nonrigid liver deformation in radiofrequency ablation interventions using image registration, Physics in Medicine & Biology 63(17) (2018) 175005.
 A.A. Novikov, D. Major, M. Wimmer, D. Lenis, K. Bühler, Deep Sequential Segmentation of Organs in Volumetric Medical Scans, IEEE transactions on medical imaging, 2018.
 Y. Huo, J.G. Terry, J. Wang, S. Nair, A. Lasko, B.I. Freedman, B.A. Landman, Fully Automatic Liver Attenuation Estimation combing CNN Segmentation and Morphological Operations, Medical physics, 2019.
 N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier, A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation, ArXiv preprint arXiv, 2019, pp. 1902.07971.
 S. Chen, K. Ma, Y. Zheng, Med3D: Transfer Learning for 3D Medical Image Analysis, ArXiv preprint arXiv, 2019, pp. 1904.00625.
 W. Tang, D. Zou, S. Yang, J. Shi, DSL: Automatic Liver Segmentation with Faster R-CNN and DeepLab, In International Conference on Artificial Neural Networks, Springer, Cham, 2018, pp. 137-147.