Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., and Jemal A., Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 74(3):229–263, 2024.

Couinaud, C., Le foie: études anatomiques et chirurgicales. Masson, 1957.

Moghbel, M., Mashohor, S., Mahmud, R., and Saripan, M., Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif. Intell. Rev. 50(4):497–537, 2018.

Tian, Y., Liu, M., Sun, Y., and Fu, S., When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects. iLIVER 2(1):73–87, 2023.

Bilic, P., Christ, P. F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.-W., Han, X., Heng, P.-A., Hesser, J., Kadoury, S., Konopczynski, T., Le, M., Li, C., Li, X., Lipkovà, J., Lowengrub, J., Meine, H., Moltz, J. H., Pal, C., Piraud, M., Qi, X., Qi, J., Rempfler, M., Roth, K., Schenk, A., Sekuboyina, A., Vorontsov, E., Zhou, P., Hülsemeyer, C., Beetz, M., Ettlinger, F., Gruen, F., Kaissis, G., Lohöfer, F., Braren, R., Holch, J., Hofmann, F., Sommer, W., Heinemann, V., Jacobs, C., Humpire Mamani, G. E., van Ginneken, B., Chartrand, G., Tang, A., Drozdzal, M., Ben-Cohen, A., Klang, E., Amitai, M. M., Konen, E., Greenspan, H., Moreau, J., Hostettler, A., Soler, L., Vivanti, R., Szeskin, A., Lev-Cohain, N., Sosna, J., Joskowicz, L., and Menze, B. H., The Liver Tumor Segmentation Benchmark (LiTS). Med. Image Anal. 84(September 2021):102680, 2019.

Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L., 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(5):1316–1325, 2020.

Article  PubMed  Google Scholar 

Yin, C., Tang, J., Yuan, T., Xu, Z., and Wang, Y., Bridging the gap between semantic segmentation and instance segmentation. IEEE Trans. Multimed. 24:4183–4196, 2021.

Article  Google Scholar 

Survarachakan, S., Ray Prasad, P. J., Naseem, R., Pérez de Frutos, J., Kumar, R. P., Langø, T., Cheikh, F. A., Elle, O. J., Lindseth, F., Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions. Artif. Intell. Med. 130:102331, 2022.

He, B., Yin, D., Chen, X., Luo, H., Xiao, D., He, M., Wang, G., Fang, C., Liu, L., and Jia, F., A study of generalization and compatibility performance of 3d u-net segmentation on multiple heterogeneous liver ct datasets. BMC Med. Imaging 21:1–13, 2021.

Article  CAS  Google Scholar 

Oliveira, R. B., Papa, J. P., Pereira, A. S., Tavares, and J. M. R. S., Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl. 29(3):613–636, 2018.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al., The prisma 2020 statement: an updated guideline for reporting systematic reviews. Bmj 372, 2021.

van Eck, N. J., and Waltman, L., Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538, 2010.

Article  PubMed  Google Scholar 

van Ginneken Tobias Heimann, B., 3D Segmentation in the Clinic: A Grand Challenge, 2007.

Du G., Deng X., MICCAI 2008 Workshop: Grand Challenge Liver Tumor Segmentation, 2008.

Goksel, O., Foncubierta-Rodríguez, A., Jiménez del Toro, O. A., Müller, H., Langs, G., Weber, M-A., Menze, B., Eggel, I., Gruenberg, K., Winterstein, M., Holzer, M., Krenn, M., Kontokotsios, G., Metallidis, S., Schaer, R., Taha, A. A., Jakab, A., Salas Fernandez, T., and Hanbury, A., Overview of the VISCERAL challenge at ISBI 2015. In: Goksel, O., et al. (Eds.) Proceedings of the VISCERAL Challenge at ISBI, number 1390 in CEUR Workshop Proceedings, pages 6–11, 2015.

Soler, L., Hostettler, A., Agnus, V., Charnoz, A., Fasquel, J.-B., Moreau, J., Osswald, A.-B., Bouhadjar, M., and Marescaux, J., 3D Image Reconstruction for Comparison of Algorithm Database: A patient-specific anatomical and medical image database. URL: https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01, 2012.

Emre, A., Kavur, N., Gezer, S., Barış, M., Aslan, S., Conze, P. H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S., Baydar, B., Lachinov, D., Han, S., Pauli, J., Isensee, F., Perkonigg, M., Sathish, R., Rajan, R., Sheet, D., Dovletov, G., Speck, O., Nürnberger, A., Maier-Hein, K. H., Bozdağı Akar, G., Ünal, G., Dicle, O., Selver, M. A., CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 2021.

Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van Ginneken, B., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., et al., A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv:1902.09063, 2019.

Zhao, L., Liver vessel segmentation. IEEE TMI, 2022.

Yuan, Y., Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation. arXiv:1710.0:null, 2017.

Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., and Greenspan, H., Fully Convolutional Network for Liver Segmentation and Lesions Detection. In: Deep Learning and Data Labeling for Medical Applications DLMIA, volume 10008. Springer, Cham, 2016.

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., and Maier-Hein, K. H., nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods, 18(2):203–211, 2020.

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., and Heng, P., H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. IEEE Trans. Medi. Imaging 37:2663–2674, 2017.

Chlebus, G., Schenk, A., Moltz, J. H., Bram van Ginneken, Horst Karl Hahn, and Hans Meine. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci. Rep. 8(1), 2018.

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al., The medical segmentation decathlon. Nat. Commun. 13(1):4128, 2022.

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A. L., and Roth, H., 3d semi-supervised learning with uncertainty-aware multi-view co-training. CoRR[SPACE]arXiv:1811.12506, 2018.

Ronneberger, O., Fischer, P., and Brox, T., U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F. (Eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Cham, Springer International Publishing, 2015.

He, K., Zhang, X., Ren, S., and Sun, J., Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December: 770–778, 2015.

Sabir, M. W., Khan, Z., Saad, N. M., Khan, D. M., Al-Khasawneh, M. A., Perveen, K., Qayyum, A., and Azhar Ali, S. S., Segmentation of Liver Tumor in CT Scan Using ResU-Net. Appl. Sci. (Switzerland), 12(17), 2022.

Li, L., and Ma, H., RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation. Sensors, 22(7), 2022.

Kushnure, D. T., and Talbar, S. N., MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images. Comput. Med. Imaging Graph., 89, 2021.

Lv, P., Wang, J., and Wang, H., 2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT. Biomed. Signal Process. Control, 75, 2022.

Yu, F., and Koltun, V., Multi-Scale Context Aggregation by Dilated Convolutions. 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, 2015.

Delmoral, J. C., Costa, D. C., Borges, D., and Tavares, J. M. R. S., Segmentation of pathological liver tissue with Dilated Fully Convolutional Networks: A Preliminary Study. 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings, 2019.

Liu, L., Wu, F. X., Wang, Y. P., and Wang, J., Multi-receptive-field CNN for semantic segmentation of medical images. IEEE J. Biomed. Health Inform., 24(11):3215–3225, 2020.

Wang, J., Lv, P., Wang, H., and Shi, C., SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Computer Methods and Programs in Biomedicine, 208, 2021.

Xie, X., Pan, X., Shao, F., Zhang, W., and An, J., MCI-Net: Multi-scale context integrated network for liver CT image segmentation. Comput. Electr. Eng., 101, 2022.

Zhou, Z., Rahman Siddiquee, Md. M., Tajbakhsh, N., and Liang, J., UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Trans. Med. Imaging 39(6):1856, 2020.

Tran, S. T., Cheng, C. H., and Liu, D. G., A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT. IEEE Access 9:3752–3764, 2021.

Fan, T., Wang, G., Li, Y., and Wang, H., Ma-net: A multi-scale attention network for liver and tumor segmentation. IEEE Access 8:179656–179665, 2020.

Article  Google Scholar 

Kushnure, D. T., and Talbar, S. N., HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images. Comput. Methods Programs Biomed., 213, 2022.

Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M. A. M., Liver Tumor Segmentation in CT Scans Using Modified SegNet. Sensors (Basel, Switzerland) 20(5), 2020.

Hao, W., Zhang, J., Su, J., Song, Y., Liu, Z., Liu, Y., Qiu, C., and Han, K., HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images. Comput. Methods Programs Biomed. 224, 2022.

Alalwan, N., Abozeid, A., ElHabshy, A. A. A., and Alzahrani, A.,Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation. Alex. Eng. J. 60(1):1231–1239, 2021.

Bahdanau, D., Cho, K. H., and Bengio, Y., Neural Machine Translation by Jointly Learning to Align and Translate. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2014.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021 - 9th International Conference on Learning Representations, 2020.

Xie, Y., Yang, B., Guan, Q., Zhang, J., Wu, Q., and Xia, Y., Attention Mechanisms in Medical Image Segmentation: A Survey. arXiv:2305.17937, 2023.

Wang, Z., Zou, Y., and Liu, P. X., Hybrid dilation and attention residual U-Net for medical image segmentation. Comput. Biol. Med. 134:104449, 2021.

Article  PubMed  Google Scholar 

Chen, Y., Zheng, C., Zhang, W., Lin, H., Chen, W., Zhang, G., Xu, G., and Wu, F., MS-FANet: Multi-scale feature attention network for liver tumor segmentation. Comput. Biol. Med. 163:107208, 2023.

Jin, Q., Meng, Z., Sun, C., Cui, H., and Su, R., RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans. Front. Bioeng. Biotechnol. 8, 2020.

Liu, H., Fu, Y., Zhang, S., Liu, J., Wang, Y., Wang, G., Fang, J., GCHA-Net: Global context and hybrid attention network for automatic liver segmentation. Comput. Biol. Med. 152(August 2022):106352, 2023.

Wang, J., Zhang, X., Lv, P., Wang, H., and Cheng, Y., Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT. J. Digit. Imaging 35(6):1479–1493, 2022.

Article  PubMed  PubMed Central  Google Scholar 

Jiang, H., Shi, T., Bai, Z., and Huang, L., AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes. IEEE Access 7:24898–24909, 2019.

Article  Google Scholar 

Li, C., Tan, Y., Chen, W., Luo, X., He, Y., Gao, Y., and Li, F., ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation. Comput. Graph. (Pergamon) 90:11–20, 2020.

Article  Google Scholar 

Li, J., Liu, K., Hu, Y., Zhang, H., Asghar, A., Chen, H., Zhang, W., Algarni, A. D., and Elmannai, H., Eres-unet++: Liver ct image segmentation based on high-efficiency channel attention and res-unet++. Comput. Biol. Med. 158:106501, 2023.

Fan, T., Wang, G., Wang, X., Li, Y., and Wang, H., Msn-net: a multi-scale context nested u-net for liver segmentation. Signal Image Video Process. 15:1089–1097, 2021.

Article  Google Scholar 

Zhou, Y., Kong, Q., Zhu, Y., and Su, Z., Mcfa-unet: Multiscale cascaded feature attention u-net for liver segmentation. IRBM (pp. 100789), 2023.

Li, L., and Ma, H., RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation. Sensors 22(7), 2022.

Chen, Y., Zheng, C., Zhou, T., Feng, L., Liu, L., Zeng, Q., and Wang, G., A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans. Comput. Biol. Medi. 152(September 2022):106421, 2023.

Wang, X., Wang, S., Zhang, Z., Yin, X., Wang, T., and Li, N., CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation. Biomed. Signal Process. Control 79(2), 2023.

Yan, Q., Wang, B., Zhang, W., Luo, C., Xu, W., Xu, Z., Zhang, Y., Shi, Q., Zhang, L., and You, Z., Attention-Guided Deep Neural Network with Multi-Scale Feature Fusion for Liver Vessel Segmentation. IEEE J. Biomed. Health Inform. 25(7):2629–2642, 2021.

Article  Google Scholar 

Tang, W., Zou, D., Yang, S., Shi, J., Dan, J., and Song, G., A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Comput. Appl. 32(11):6769–6778, 2020.

Novikov, A. A., Major, D., Wimmer, M., Lenis, D., and Buhler, K., Deep sequential segmentation of organs in volumetric medical scans. IEEE Trans. Medi. Imaging 38(5):1207–1215, 2019.

Article  PubMed  Google Scholar 

Zhang, J., Xie, Y., Zhang, P., Chen, H., Xia, Y., and Shen, C., Light-weight hybrid convolutional network for liver tumor segmentation. IJCAI International Joint Conference on Artificial Intelligence, 2019-August: 4271–4277, 2019.

Kitrungrotsakul, T., Han, X. H., Iwamoto, Y., Lin, L., Foruzan, A. H., Xiong, W., and Chen, Y. W., VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation. Comput. Med. Imaging Graph. 75:74–83, 2019.

Sandfort, V., Yan, K., Pickhardt, P. J., and Summers, R. M., Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci. Rep. 9(1), 2019.

Pang, S., Du, A., Orgun, M. A., Yu, Z., Wang, Y., Wang, Y., and Liu, G., Ctumorgan: a unified framework for automatic computed tomography tumor segmentation. Eur. J. Nucl. Med. Mol. Imaging (pp. 1–21), 2020.

Xia, K., Yin, H., Qian, P., Jiang, Y., and Wang, S., Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal ct images. IEEE Access 7:96349–96358, 2019.

Article  Google Scholar 

Rezaei, M., Näppi, J. J., Lippert, C., Meinel, C., and Yoshida, H., Generative multi-adversarial network for striking the right balance in abdominal image segmentation. Int. J. Comput. Assist. Radiol. Surg. 15:1847–1858, 2020.

Demir, U., Zhang, Z., Wang, B., Antalek, M., Keles, E., Jha, D., Borhani, A., Ladner, D., and Bagci, U., Transformer based generative adversarial network for liver segmentation. In: International Conference on Image Analysis and Processing (pp. 340–347). Springer, 2022.

Liu, Y., Yang, F., and Yang, Y., A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation. J. Appl. Clin. Med. Phys. 24(4):e13927, 2023.

Article  PubMed  PubMed Central  Google Scholar 

Chlebus, G., Schenk, A., Hahn, H. K., Van Ginneken, B., and Meine, H., Robust Segmentation Models Using an Uncertainty Slice Sampling-Based Annotation Workflow. IEEE Access 10:4728–4738, 2022.

Article  Google Scholar 

Hansen, S., Gautam, S., Salahuddin, S. A., Kampffmeyer, M., and Jenssen, R., ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. Med. Image Anal. 89:102870, 2023.

Couteaux, V., Nempont, O., Pizaine, G., Bloch, I., Towards interpretability of segmentation networks by analyzing deepDreams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11797 LNCS:56–63, 2019.

Khan, R. A., Luo, Y., and Wu, F. X., RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation. Artif. Intell. Med. 124, 2022.

Li, R., Xu, L., Xie, K., Song, J., Ma, X., Chang, L., and Yan, Q., Dht-net: Dynamic hierarchical transformer network for liver and tumor segmentation. IEEE J. Biomed. Health Inform. 2023.

Liu, L., Wang, Y., Chang, J., Zhang, P., Liang, G., and Zhang, H., LLRHNet: Multiple Lesions Segmentation Using Local-Long Range Features. Front. Neuroinform. 16, 2022.

Gao, Z., Zong, Q., Wang, Y., Yan, Y., Wang, Y., Zhu, N., Zhang, J., Wang, Y., and Zhao, L., Laplacian salience-gated feature pyramid network for accurate liver vessel segmentation. IEEE Trans. Med. Imaging 42(10):3059–3068, 2023.

Article  PubMed  Google Scholar 

Wu, M., Qian, Y., Liao, X., Wang, Q., and Heng, P.-A., Hepatic vessel segmentation based on 3d swin-transformer with inductive biased multi-head self-attention. BMC Med. Imaging 23(1):91, 2023.

Article  Google Scholar 

Su, J., Liu, Z., Zhang, J., Sheng, V. S., Song, Y., Zhu, Y., and Liu, Y., DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowl.-Based Syst. 232, 2021.

Qin, D., Bu, J.-J., Liu, Z., Shen, X., Zhou, S., Gu, J.-J., Wang, Z.-H., Wu, L., and Dai, H.-F., Efficient medical image segmentation based on knowledge distillation. IEEE Trans. Med. Imaging 40(12):3820–3831, 2021.

Article  PubMed  Google Scholar 

Özcan, F., Uçan, O. N., Karaçam, S., and Tunçman, D., Fully automatic liver and tumor segmentation from ct image using an aim-unet. Bioengineering 10(2):215, 2023.

Tong, G., Jiang, H., and Yao, Y.-D., Sda-unet: a hepatic vein segmentation network based on the spatial distribution and density awareness of blood vessels. Phys. Med. Biol. 68(3):035009, 2023.

Article  Google Scholar 

Yang, C.-J., Wang, C.-K., Dean Fang, Y.-H., Wang, J.-Y., Su, F.-C., Tsai, H.-M., Lin, Y.-J., Tsai, H.-W., and Yeh, L.-R., Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets. PloS One 16(8):e0255605, 2021.

Chen, Y., Wang, K., Liao, X., Qian, Y., Wang, Q., Yuan, Z., and Heng, P. A., Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation. Front. Genet. 10:492928, 2019.

Chi, J., Han, X., Wu, C., Wang, H., and Ji, P., X-Net: Multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans. Neurocomputing 459:81–96, 2021.

Lei, T., Wang, R., Zhang, Y., Wan, Y., Liu, C., and Nandi, A. K., DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation. IEEE Trans. Radiat. Plasma Med. Sci. 6(1):68–78, 2022.

Lv, P., Wang, J., Zhang, X., and Shi, C., Deep supervision and atrous inception-based u-net combining crf for automatic liver segmentation from ct. Sci. Rep. 12(1):16995, 2022.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bogoi, S., and Udrea, A., A lightweight deep learning approach for liver segmentation. Mathematics 11(1), 2023.

Zhang, L., Liu, J., Li, D., Liu, J., and Liu, X.., Msaa-net: a multi-scale attention-aware u-net is used to segment the liver. Signal Image Video Process. 17(4):1001–1009, 2023.

Article