Liver tumor segmentation method based on U-Net architecture: a review
DOI:
https://doi.org/10.4108/eetel.5263Keywords:
CT scans, Liver tumor segmentation, Deep learning, U-Net, 2D, 3D, 2.5DAbstract
Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.
References
M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. J. A. I. R. Saripan, "Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography," vol. 50, pp. 497-537, 2018.
S. J. J. o. C. Luo and Communications, "Review on the methods of automatic liver segmentation from abdominal images," vol. 2, no. 02, p. 1, 2014.
Y.-D. Zhang et al., "Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation," vol. 64, pp. 149-187, 2020.
S. Wang et al., "Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects," vol. 76, pp. 376-421, 2021.
Y. Zhang et al., "Deep Learning in Food Category Recognition," p. 101859, 2023.
J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440.
O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 2015, pp. 234-241: Springer.
P. Bilic et al., "The liver tumor segmentation benchmark (lits)," vol. 84, p. 102680, 2023.
F. Quinton et al., "A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma," vol. 8, no. 5, p. 79, 2023.
Z. Liu et al., "Liver CT sequence segmentation based with improved U-Net and graph cut," vol. 126, pp. 54-63, 2019.
K. Clark et al., "The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository," vol. 26, pp. 1045-1057, 2013.
M. Drozdzal et al., "Learning normalized inputs for iterative estimation in medical image segmentation," vol. 44, pp. 1-13, 2018.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "Synthetic data augmentation using GAN for improved liver lesion classification," in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), 2018, pp. 289-293: IEEE.
H. R. Roth et al., "An application of cascaded 3D fully convolutional networks for medical image segmentation," vol. 66, pp. 90-99, 2018.
C. Sun et al., "Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs," vol. 83, pp. 58-66, 2017.
L. R. J. E. Dice, "Measures of the amount of ecologic association between species," vol. 26, no. 3, pp. 297-302, 1945.
P. J. N. p. Jaccard, "The distribution of the flora in the alpine zone. 1," vol. 11, no. 2, pp. 37-50, 1912.
L. Chen et al., "Liver tumor segmentation in CT volumes using an adversarial densely connected network," vol. 20, pp. 1-13, 2019.
K. Simonyan and A. J. a. p. a. Zisserman, "Very deep convolutional networks for large-scale image recognition," 2014.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, "Ghostnet: More features from cheap operations," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1580-1589.
P.-H. Conze et al., "Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks," vol. 117, p. 102109, 2021.
H. Seo, C. Huang, M. Bassenne, R. Xiao, and L. J. I. t. o. m. i. Xing, "Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images," vol. 39, no. 5, pp. 1316-1325, 2019.
E. K. Wang, C.-M. Chen, M. M. Hassan, and A. J. F. G. C. S. Almogren, "A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain," vol. 108, pp. 135-144, 2020.
S.-T. Tran, C.-H. Cheng, and D.-G. J. I. A. Liu, "A multiple layer U-Net, U n-Net, for liver and liver tumor segmentation in CT," vol. 9, pp. 3752-3764, 2020.
S. Guan, A. A. Khan, S. Sikdar, P. V. J. I. j. o. b. Chitnis, and h. informatics, "Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal," vol. 24, no. 2, pp. 568-576, 2019.
X. Xiao, S. Lian, Z. Luo, and S. Li, "Weighted res-unet for high-quality retina vessel segmentation," in 2018 9th international conference on information technology in medicine and education (ITME), 2018, pp. 327-331: IEEE.
F. Ghofrani, H. Behnam, and H. D. K. Motlagh, "Liver Segmentation in CT Images Using Deep Neural Networks," in 2020 28th Iranian Conference on Electrical Engineering (ICEE), 2020, pp. 1-6: IEEE.
W. Zhao et al., "Liver segemtation in CT image with no-edge-cuting UNet," in 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2020, vol. 9, pp. 2315-2318: IEEE.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. J. I. t. o. m. i. Liang, "Unet++: Redesigning skip connections to exploit multiscale features in image segmentation," vol. 39, no. 6, pp. 1856-1867, 2019.
Q. Gao, M. J. C. i. B. Almekkawy, and Medicine, "ASU-Net++: A nested U-Net with adaptive feature extractions for liver tumor segmentation," vol. 136, p. 104688, 2021.
N. Abraham and N. M. Khan, "A novel focal tversky loss function with improved attention u-net for lesion segmentation," in 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), 2019, pp. 683-687: IEEE.
O. Oktay et al., "Attention u-net: Learning where to look for the pancreas," 2018.
C. Li et al., "Attention unet++: A nested attention-aware u-net for liver ct image segmentation," in 2020 IEEE international conference on image processing (ICIP), 2020, pp. 345-349: IEEE.
Z. Wang, Y. Zou, P. X. J. C. i. b. Liu, and medicine, "Hybrid dilation and attention residual U-Net for medical image segmentation," vol. 134, p. 104449, 2021.
S. Pang, A. Du, M. A. Orgun, Y. Wang, and Z. J. N. N. Yu, "Tumor attention networks: Better feature selection, better tumor segmentation," vol. 140, pp. 203-222, 2021.
C. Li et al., "ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation," vol. 90, pp. 11-20, 2020.
N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, and M. Haltmeier, "A joint deep learning approach for automated liver and tumor segmentation," in 2019 13th International conference on Sampling Theory and Applications (SampTA), 2019, pp. 1-5: IEEE.
S. LIa and G. K. Tsoa, "Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation."
P. F. Christ et al., "Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, 2016, pp. 415-423: Springer.
O. I. J. J. o. A. C. M. P. Alirr, "Deep learning and level set approach for liver and tumor segmentation from CT scans," vol. 21, no. 10, pp. 200-209, 2020.
Y. Zhang et al., "Deep learning initialized and gradient enhanced level-set based segmentation for liver tumor from CT images," vol. 8, pp. 76056-76068, 2020.
F. Milletari, N. Navab, and S.-A. Ahmadi, "V-net: Fully convolutional neural networks for volumetric medical image segmentation," in 2016 fourth international conference on 3D vision (3DV), 2016, pp. 565-571: Ieee
T. Lei, W. Zhou, Y. Zhang, R. Wang, H. Meng, and A. K. Nandi, "Lightweight v-net for liver segmentation," in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 1379-1383: IEEE.
Y. Zhang, X. Pan, C. Li, and T. J. A. S. Wu, "3D liver and tumor segmentation with CNNs based on region and distance metrics," vol. 10, no. 11, p. 3794, 2020.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, "3D U-Net: learning dense volumetric segmentation from sparse annotation," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, 2016, pp. 424-432: Springer.
S. Mohagheghi, A. H. J. I. j. o. c. a. r. Foruzan, and surgery, "Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs," vol. 15, pp. 249-257, 2020.
Q. Jin, Z. Meng, C. Sun, H. Cui, R. J. F. i. B. Su, and Biotechnology, "RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans," vol. 8, p. 605132, 2020.
L. Song, K. Geoffrey, and H. J. E. S. w. A. Kaijian, "Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation," vol. 145, p. 113131, 2020.
Q. Dou et al., "3D deeply supervised network for automated segmentation of volumetric medical images," vol. 41, pp. 40-54, 2017.
F. Lu, F. Wu, P. Hu, Z. Peng, D. J. I. j. o. c. a. r. Kong, and surgery, "Automatic 3D liver location and segmentation via convolutional neural network and graph cut," vol. 12, pp. 171-182, 2017.
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. J. I. t. o. m. i. Heng, "H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes," vol. 37, no. 12, pp. 2663-2674, 2018.
S. Wang et al., "Conquering data variations in resolution: A slice-aware multi-branch decoder network," vol. 39, no. 12, pp. 4174-4185, 2020.
L. Zhang and S. C.-H. J. a. p. a. Yu, "Context-aware PolyUNet for liver and lesion segmentation from abdominal CT images," 2021.
A. Ben-Cohen et al., "Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection," vol. 78, pp. 186-194, 2019.
Z. Wang and G. J. I. J. o. P. E. Wang, "Triplanar convolutional neural network for automatic liver and tumor image segmentation," vol. 14, no. 12, p. 3151, 2018.
Y. Ahn et al., "Deep learning algorithm for automated segmentation and volume measurement of the liver and spleen using portal venous phase computed tomography images," vol. 21, no. 8, p. 987, 2020.
C. Zhang, Q. Hua, Y. Chu, P. J. C. i. B. Wang, and Medicine, "Liver tumor segmentation using 2.5 D UV-Net with multi-scale convolution," vol. 133, p. 104424, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
