Martin J, Petrillo A, Smyth EC, Shaida N, Khwaja S, Cheow H, Duckworth A, Heister P, Praseedom R, Jah A, Balakrishnan A, Harper S, Liau S, Kosmoliaptsis V, Huguet E (2020) Colorectal liver metastases: current management and future perspectives. World J Clin Oncol 11(10):761–808. https://doi.org/10.5306/wjco.v11.i10.761
Article PubMed PubMed Central Google Scholar
Patel RK, Rahman S, Schwantes IR, Bartlett A, Eil R, Farsad K, Fowler K, Goodyear SM, Hansen L, Kardosh A, Nabavizadeh N, Rocha FG, Tsikitis VL, Wong MH, Mayo SC (2023) Updated management of colorectal cancer liver metastases: scientific advances driving modern therapeutic innovations. Cell Mol Gastroenterol Hepatol 16(6):881–894. https://doi.org/10.1016/j.jcmgh.2023.08.012
Article CAS PubMed PubMed Central Google Scholar
Joskowicz L, Cohen D, Caplan N, Sosna J (2018) Inter-observer variability of manual contour delineation of structures in ct. Eur Radiol 29(3):1391–1399. https://doi.org/10.1007/s00330-018-5695-5
Gupta Aashish C., Cazoulat Guillaume, Al Taie Mais, Yedururi Sireesha, Rigaud Bastien, Castelo Austin, Wood John, Yu Cenji, O’Connor Caleb, Salem Usama, Silva Jessica Albuquerque Marques, Jones Aaron Kyle, McCulloch Molly, Odisio Bruno C., Koay Eugene J., Brock Kristy K. (2024) Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images. Sci Rep. https://doi.org/10.1038/s41598-024-53997-y
Article PubMed PubMed Central Google Scholar
Conze P-H, Andrade-Miranda G, Singh VK, Jaouen V, Visvikis D (2023) Current and emerging trends in medical image segmentation with deep learning. IEEE Trans Radiat Plasma Med Sci 7(6):545–569. https://doi.org/10.1109/TRPMS.2023.3265863
Conze P-H, Kavur AE, Cornec-Le Gall E, Gezer NS, Le Meur Y, Selver MA, Rousseau F (2021) Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. Artif Intell Med 117:102109. https://doi.org/10.1016/j.artmed.2021.102109
Ma J, Zhang Y, Gu S, An X, Wang Z, Ge C, Wang C, Zhang F, Wang Y, Xu Y et al (2022) Fast and low-GPU-memory abdomen CT organ segmentation: the FLARE challenge. Med Image Anal 82:102616
Messaoudi H, Belaid A, Ben Salem D, Conze P-H (2023) Cross-dimensional transfer learning in medical image segmentation with deep learning. Med Image Anal 88:102868. https://doi.org/10.1016/j.media.2023.102868
Han X, Wu X, Wang S, Xu L, Xu H, Zheng D, Yu N, Hong Y, Yu Z, Yang D, Yang Z (2022) Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network. Insights Imaging. https://doi.org/10.1186/s13244-022-01163-1
Article PubMed PubMed Central Google Scholar
Xie T, Li Y, Lin Z, Liu X, Zhang X, Zhang Y, Zhang D, Cheng G, Wang X (2023) Deep learning for fully automated segmentation and volumetry of couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model. Quant Imaging Med Surg 13(5):3088–3103. https://doi.org/10.21037/qims-22-1008
Article PubMed PubMed Central Google Scholar
Le DC, Chansangrat J, Keeratibharat N, Horkaew P (2021) Functional segmentation for preoperative liver resection based on hepatic vascular networks. IEEE Access 9:15485–15498. https://doi.org/10.1109/access.2021.3053384
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. CoRR arxiv: 1505.04597
Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) 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, Los Alamitos, CA, USA. https://doi.org/10.1109/WACV51458.2022.00181
Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2022) Swin unetr: Swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi A, Bakas S (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham, pp 272–284
Tan M, Le Q (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6105–6114. https://proceedings.mlr.press/v97/tan19a.html
Nam H, Kim H-E (2018) Batch-instance normalization for adaptively style-invariant neural networks. In: Advances in Neural Information Processing Systems, vol. 31
Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ (2016) Deep networks with stochastic depth. In: European Conference on Computer Vision, pp. 646–661
Simpson AL, Peoples J, Creasy JM, Fichtinger G, Gangai N, Lasso A, Keshava Murthy KN, Shia J, D’Angelica MI, Do RKG (2023) Preoperative CT and Survival Data for Patients Undergoing Resection of Colorectal Liver Metastases. The Cancer Imaging Archive. https://doi.org/10.7937/QXK2-QG03 . https://wiki.cancerimagingarchive.net/x/TIBPBQ
Simpson AL, Doussot A, Creasy JM, Adams LB, Allen PJ, DeMatteo RP, Gönen M, Kemeny NE, Kingham TP, Shia J et al (2017) Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol 24:2482–2490
Article PubMed PubMed Central Google Scholar
Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, Szeskin A, Jacobs C, Mamani GEH, Chartrand G et al (2023) The liver tumor segmentation benchmark (lits). Med Image Anal 84:102680. https://doi.org/10.1016/j.media.2022.102680
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