Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
Citation: Wencong Zhang, Yuxi Tao, Zhanyao Huang, Yue Li, Yingjia Chen, Tengfei Song, Xiangyuan Ma, Yaqin Zhang. Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5735-5761. doi: 10.3934/mbe.2024253
Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
| [1] |
H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al., Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 71 (2021), 209–249. https://doi.org/10.3322/caac.21660 doi: 10.3322/caac.21660
|
| [2] |
J. M. Llovet, R. K. Kelley, A. Villanueva, A. G. Singal, E. Pikarsky, S. Roayaie, et al., Hepatocellular carcinoma, Nat. Rev. Dis. Primers, 7 (2021), 6. https://doi.org/10.1038/s41572-020-00240-3 doi: 10.1038/s41572-020-00240-3
|
| [3] |
F. X. Bosch, J. Ribes, M. Díaz, R. Cléries, Primary liver cancer: worldwide incidence and trends, Gastroenterology, 127 (2004), S5–S16. https://doi.org/10.1053/j.gastro.2004.09.011 doi: 10.1053/j.gastro.2004.09.011
|
| [4] |
X. Wu, J. Li, C. Wang, G. Zhang, N. Zheng, X. Wang, Application of different imaging methods in the early diagnosis of primary hepatic carcinoma, Gastroenterol. Res. Pract., 2016 (2016), 8763205. https://doi.org/10.1155/2016/8763205 doi: 10.1155/2016/8763205
|
| [5] | K. Song, D. Wu, Shared decision-making in the management of patients with inflammatory bowel disease, World J. Gastroenterol., 28 (2022), 3092–3100. https://doi.org/10.3748%2Fwjg.v28.i26.3092 |
| [6] |
C. Chang, H. Chen, Y. Chang, M. Yang, C. Lo, W. Ko, et al., Computer-aided diagnosis of liver tumors on computed tomography images, Comput. Methods Programs Biomed., 145 (2017), 45–51. https://doi.org/10.1016/j.cmpb.2017.04.008 doi: 10.1016/j.cmpb.2017.04.008
|
| [7] |
W. Li, F. Jia, Q. Hu, Automatic segmentation of liver tumor in CT images with deep convolutional neural networks, J. Comput. Commun., 3 (2015), 146–151. http://dx.doi.org/10.4236/jcc.2015.311023 doi: 10.4236/jcc.2015.311023
|
| [8] |
R. Naseem, Z. A. Khan, N. Satpute, A. Beghdadi, F. A. Cheikh, J. Olivares, Cross-modality guided contrast enhancement for improved liver tumor image segmentation, IEEE Access, 9 (2021), 118154–118167. https://doi.org/10.1109/ACCESS.2021.3107473 doi: 10.1109/ACCESS.2021.3107473
|
| [9] |
L. Wang, M. Wu, R. Li, X. Xu, C. Zhu, X. Feng, MVI-Mind: A novel deep-learning strategy using computed tomography (CT)-based radiomics for end-to-end high efficiency prediction of microvascular invasion in hepatocellular carcinoma, Cancers, 14 (2022), 2956. https://doi.org/10.3390/cancers14122956 doi: 10.3390/cancers14122956
|
| [10] |
Y. Jiang, S. Cao, S. Cao, J. Chen, G. Wang, W. Shi, et al., Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning, J. Cancer Res. Clin. Oncol., 147 (2021), 821–833. https://doi.org/10.1007/s00432-020-03366-9 doi: 10.1007/s00432-020-03366-9
|
| [11] |
A. Radtke, S. Nadalin, G. C. Sotiropoulos, E. P. Molmenti, T. Schroeder, C. Valentin-Gamazo, et al., Computer-assisted operative planning in adult living donor liver transplantation: A new way to resolve the dilemma of the middle hepatic vein, World J. Surg., 31 (2007), 175–185. https://doi.org/10.1007/s00268-005-0718-1 doi: 10.1007/s00268-005-0718-1
|
| [12] |
P. Liang, Y. Wang, X. Yu, B. Dong, Malignant liver tumors: treatment with percutaneous microwave ablation—complications among cohort of 1136 patients, Radiology, 251 (2009), 933–940. https://doi.org/10.1148/radiol.2513081740 doi: 10.1148/radiol.2513081740
|
| [13] |
S. Gul, M. S. Khan, A. Bibi, A. Khandakar, M. A. Ayari, M. E. H. Chowdhury, Deep learning techniques for liver and liver tumor segmentation: A review, Comput. Biol. Med., 147 (2022), 105620. https://doi.org/10.1016/j.compbiomed.2022.105620 doi: 10.1016/j.compbiomed.2022.105620
|
| [14] |
L. Soler, H. Delingette, G. Malandain, J. Montagnat, N. Ayache, C. Koehl, et al., Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery, Comput. Aided Surg., 6 (2001), 131–142. https://doi.org/10.3109/10929080109145999 doi: 10.3109/10929080109145999
|
| [15] | H. A. Nugroho, D. Ihtatho, H. Nugroho, Contrast enhancement for liver tumor identification, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 41 (2008), 201. https://doi.org/10.54294/1uhwld |
| [16] |
M. Esfandiarkhani, A. H. Foruzan, A generalized active shape model for segmentation of liver in low-contrast CT volumes, Comput. Biol. Med., 82 (2017), 59–70. https://doi.org/10.1016/j.compbiomed.2017.01.009 doi: 10.1016/j.compbiomed.2017.01.009
|
| [17] | D. Wong, J. Liu, F. Yin, Q. Tian, W. Xiong, J. Zhou, et al., A semi-automated method for liver tumor segmentation based on 2D region growing with knowledge-based constraints, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 41 (2008), 159. https://doi.org/10.54294/25etax |
| [18] | L. Fernandez-de-Manuel, J. L. Rubio, M. J. Ledesma-Carbayo, J. Pascau, J. M. Tellado, E. Ramon, et al., 3D liver segmentation in preoperative CT images using a level-sets active surface method, in International Conference of the IEEE Engineering in Medicine and Biology Society, (2009), 3625–3628. https://doi.org/10.1109/iembs.2009.5333760 |
| [19] |
S. S. Kumar, R. S. Moni, J. Rajeesh, An automatic computer-aided diagnosis system for liver tumours on computed tomography images, Comput. Electr. Eng., 39 (2013), 1516–1526. https://doi.org/10.1016/j.compeleceng.2013.02.008 doi: 10.1016/j.compeleceng.2013.02.008
|
| [20] | R. Kaur, L. Kaur, S. Gupta, Enhanced K-mean clustering algorithm for liver image segmentation to extract cyst region, in IJCA Special Issue on Novel Aspects of Digital Imaging Applications, 1 (2011), 59–66. |
| [21] |
T. Zhou, S. Canu, S. Ruan, Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation, Comput. Med. Imaging Graphics, 86 (2020), 101811. https://doi.org/10.1016/j.compmedimag.2020.101811 doi: 10.1016/j.compmedimag.2020.101811
|
| [22] |
J. Dolz, K. Gopinath, J. Yuan, H. Lombaert, C. Desrosiers, I. B. Ayed, HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation, IEEE Trans. Med. Imaging, 38 (2018), 1116–1126. https://doi.org/10.1109/TMI.2018.2878669 doi: 10.1109/TMI.2018.2878669
|
| [23] |
Q. Yu, Y. Shi, J. Sun, Y. Gao, J. Zhu, Y. Dai, Crossbar-net: a novel convolutional neural network for kidney tumor segmentation in CT images, IEEE Trans. Image Process., 28 (2019), 4060–4074. https://doi.org/10.1109/TIP.2019.2905537 doi: 10.1109/TIP.2019.2905537
|
| [24] |
X. Ma, L. M. Hadjiiski, J. Wei, H. P. Chan, K. H. Cha, R. H. Cohan, et al., U‐Net based deep learning bladder segmentation in CT urography, Med. Phys., 46 (2019), 1752–1765. https://doi.org/10.1002/mp.13438 doi: 10.1002/mp.13438
|
| [25] | P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, et al., 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, (2016), 415–423. https://doi.org/10.1007/978-3-319-46723-8_48 |
| [26] |
G. Chlebus, A. Schenk, J. H. Moltz, B. van Ginneken, H. K. Hahn, H. Meine, Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing, Sci. Rep., 8 (2018), 15497. https://doi.org/10.1038/s41598-018-33860-7 doi: 10.1038/s41598-018-33860-7
|
| [27] | 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, (2015), 234–241. https://doi.org/10.1007/978-3-319-24574-4_28 |
| [28] | C. Li, Y. Tan, W. Chen, X. Luo, Y. Gao, X. Jia, et al., Attention Unet++: A nested attention-aware U-Net for liver CT image segmentation, in IEEE International Conference on Image Processing, (2020), 345–349. https://doi.org/10.1109/ICIP40778.2020.9190761 |
| [29] | H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, et al., Unet 3+: A full-scale connected unet for medical image segmentation, in IEEE International Conference on Acoustics, Speech and Signal Processing, (2020), 1055–1059. https://doi.org/10.1109/ICASSP40776.2020.9053405 |
| [30] |
H. Seo, C. Huang, M. Bassenne, R. Xiao, L. Xing, Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images, IEEE Trans. Med. Imaging, 39 (2019), 1316–1325. https://doi.org/10.1109/TMI.2019.2948320 doi: 10.1109/TMI.2019.2948320
|
| [31] |
D. T. Kushnure, S. N. Talbar, MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images, Comput. Med. Imaging Graphics, 89 (2021), 101885. https://doi.org/10.1016/j.compmedimag.2021.101885 doi: 10.1016/j.compmedimag.2021.101885
|
| [32] |
X. Xu, Q. Zhu, H. Ying, J. Li, X. Cai, S. Li, et al., A knowledge-guided framework for fine-grained classification of liver lesions based on multi-phase CT images, IEEE J. Biomed. Health Inf., 27 (2023), 386–396. https://doi.org/10.1109/JBHI.2022.3220788 doi: 10.1109/JBHI.2022.3220788
|
| [33] |
W. Shi, S. Kuang, S. Cao, B. Hu, S. Xie, S. Chen, et al., Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol, Abdom. Radiol., 45 (2020), 2688–2697. https://doi.org/10.1007/s00261-020-02485-8 doi: 10.1007/s00261-020-02485-8
|
| [34] |
Y. Xu, M. Cai, L. Lin, Y. Zhang, H. Hu, Z. Peng, et al., PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images, Med. Phys., 48 (2021), 3752–3766. https://doi.org/10.1002/mp.14922 doi: 10.1002/mp.14922
|
| [35] |
I. R. Kamel, M. A. Choti, K. M. Horton, H. J. V. Braga, B. A. Birnbaum, E. K. Fishman, et al., Surgically staged focal liver lesions: accuracy and reproducibility of dual-phase helical CT for detection and characterization, Radiology, 227 (2003), 752–757. https://doi.org/10.1148/radiol.2273011768 doi: 10.1148/radiol.2273011768
|
| [36] |
F. Ouhmich, V. Agnus, V. Noblet, F. Heitz, P. Pessaux, Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks, Int. J. Comput. Assisted Radiol. Surg., 14 (2019), 1275–1284. https://doi.org/10.1007/s11548-019-01989-z doi: 10.1007/s11548-019-01989-z
|
| [37] |
C. Sun, S. Guo, H. Zhang, J. Li, M. Chen, S. Ma, et al., Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs, Artif. Intell. Med., 83 (2017), 58–66. https://doi.org/10.1016/j.artmed.2017.03.008 doi: 10.1016/j.artmed.2017.03.008
|
| [38] | Y. Wu, Q. Zhou, H. Hu, G. Rong, Y. Li, S. Wang, Hepatic lesion segmentation by combining plain and contrast-enhanced CT images with modality weighted U-Net, in IEEE International Conference on Image Processing, (2019), 255–259. https://doi.org/10.1109/ICIP.2019.8802942 |
| [39] | Y. Zhang, C. Peng, L. Peng, H. Huang, R. Tong, L. Lin, et al., Multi-phase liver tumor segmentation with spatial aggregation and uncertain region inpainting, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2021), 68–77. https://doi.org/10.1007/978-3-030-87193-2_7 |
| [40] | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in Advances in Neural Information Processing Systems, 30 (2017). |
| [41] | L. Wang, X. Wang, B. Zhang, X. Huang, C. Bai, M. Xia, et al., Multi-scale Hierarchical Transformer structure for 3D medical image segmentation, in IEEE International Conference on Bioinformatics and Biomedicine, (2021), 1542–1545. https://doi.org/10.1109/BIBM52615.2021.9669799 |
| [42] | H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, et al., Swin-unet: Unet-like pure transformer for medical image segmentation, in European Conference on Computer Vision, (2021), 205–218. https://doi.org/10.1007/978-3-031-25066-8_9 |
| [43] | J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, et al., Transunet: Transformers make strong encoders for medical image segmentation, preprint, arXiv: 2102.04306. https://doi.org/10.48550/arXiv.2102.04306 |
| [44] |
H. Xiao, L. Li, Q. Liu, X. Zhu, Q. Zhang, Transformers in medical image segmentation: A review, Biomed. Signal Process., 84 (2023), 104791. https://doi.org/10.1016/j.bspc.2023.104791 doi: 10.1016/j.bspc.2023.104791
|
| [45] |
K. He, C. Gan, Z. Li, I. Rekik, Z. Yin, W. Ji, et al., Transformers in medical image analysis, Intell. Med., 3 (2023), 59–78. https://doi.org/10.1016/j.imed.2022.07.002 doi: 10.1016/j.imed.2022.07.002
|
| [46] |
Y. Xu, X. He, G. Xu, G. Qi, K. Yu, L. Yin, et al., A medical image segmentation method based on multi-dimensional statistical features, Front. Neurosci., 16 (2022), 1009581. https://doi.org/10.3389/fnins.2022.1009581 doi: 10.3389/fnins.2022.1009581
|
| [47] |
X. He, G. Qi, Z. Zhu, Y. Li, B. Cong, L. Bai, Medical image segmentation method based on multi-feature interaction and fusion over cloud computing, Simul. Modell. Pract. Theory, 126 (2023), 102769. https://doi.org/10.1016/j.simpat.2023.102769 doi: 10.1016/j.simpat.2023.102769
|
| [48] | A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, D. Xu, Swin unetr: Swin transformers for semantic segmentation of brain tumors in MRI images, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2021), 272–284. https://doi.org/10.1007/978-3-031-08999-2_22 |
| [49] |
Z. Zhu, X. He, G. Qi, Y. Li, B. Cong, Y. Liu, Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI, Inf. Fusion, 91 (2023), 376–387. https://doi.org/10.1016/j.inffus.2022.10.022 doi: 10.1016/j.inffus.2022.10.022
|
| [50] |
Y. Li, Z. Wang, L. Yin, Z. Zhu, G. Qi, Y. Liu, X-Net: a dual encoding–decoding method in medical image segmentation, Visual Comput., 39 (2023), 2223–2233. https://doi.org/10.1007/s00371-021-02328-7 doi: 10.1007/s00371-021-02328-7
|
| [51] | J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, V. M. Patel, Medical Transformer: Gated axial-attention for medical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2021), 36–46. https://doi.org/10.1007/978-3-030-87193-2_4 |
| [52] | E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, P. Luo, SegFormer: Simple and efficient design for semantic segmentation with transformers, in Advances in Neural Information Processing Systems, 34 (2021), 12077–12090. |
| [53] | A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al., An image is worth 16x16 words: Transformers for image recognition at scale, in International Conference on Learning Representations, preprint, arXiv: 2010.11929. https://doi.org/10.48550/arXiv.2010.11929 |
| [54] |
C. Peng, Y. Zhang, J. Zheng, B. Li, J. Shen, M. Li, et al., IMⅡN: an inter-modality information interaction network for 3D multi-modal breast tumor segmentation, Comput. Med. Imaging Graphics, 95 (2022), 102021. https://doi.org/10.1016/j.compmedimag.2021.102021 doi: 10.1016/j.compmedimag.2021.102021
|
| [55] | L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z. H. Jiang, et al., Tokens-to-token vit: Training vision transformers from scratch on imagenet, in Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021), 558–567. https://doi.org/10.48550/arXiv.2101.11986 |
| [56] | N. Liu, N. Zhang, K. Wan, L. Shao, J. Han, Visual saliency transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021), 4722–4732. |
| [57] | A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, et al., Automatic differentiation in pytorch, in Advances in Neural Information Processing Systems, 2017. |
| [58] | D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, preprint, arXiv: 1412.6980. https://doi.org/10.48550/arXiv.1412.6980 |
| [59] | W. Luo, Y. Li, R. Urtasun, R. Zemel, Understanding the effective receptive field in deep convolutional neural networks, in Advances in Neural Information Processing Systems, 29 (2016). |
| [60] |
W. Zhou, W. Jian, X. Cen, L. Zhang, H. Guo, Z. Liu, et al., Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks, Front. Oncol., 11 (2021), 588010. https://doi.org/10.3389/fonc.2021.588010 doi: 10.3389/fonc.2021.588010
|
| [61] |
X. Zhong, H. Long, L. Su, R. Zheng, W. Wang, Y. Duan, et al., Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis, Abdom. Radiol., 47 (2022), 2071–2088. https://doi.org/10.1007/s00261-022-03496-3 doi: 10.1007/s00261-022-03496-3
|
| [62] |
K. Bera, N. Braman, A. Gupta, V. Velcheti, A. Madabhushi, Predicting cancer outcomes with radiomics and artificial intelligence in radiology, Nat. Rev. Clin. Oncol., 19 (2022), 132–146. https://doi.org/10.1038/s41571-021-00560-7 doi: 10.1038/s41571-021-00560-7
|
| [63] |
J. Liu, D. Cheng, Y. Liao, C. Luo, Q. Lei, X. Zhang, et al., Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma, Quant. Imaging Med. Surg., 13 (2023), 3948–3961. https://doi.org/10.21037/qims-22-1011 doi: 10.21037/qims-22-1011
|
| [64] |
J. J. M. Van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, et al., Computational radiomics system to decode the radiographic phenotype, Cancer Res., 77 (2017), e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339 doi: 10.1158/0008-5472.CAN-17-0339
|
Figures(7) / Tables(6)
Wencong Zhang, Yuxi Tao, Zhanyao Huang, Yue Li, Yingjia Chen, Tengfei Song, Xiangyuan Ma, Yaqin Zhang. Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5735-5761. doi: 10.3934/mbe.2024253
DownLoad: