3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Untitled work

(2010) · Cited by 14,134

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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908

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Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135

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Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679

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Kainmüller, D., Lange, T., & Lamecker, H. (2007) · Cited by 173

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Wimmer, A., Soza, G., & Hornegger, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3_4 · Cited by 74

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Heimann, T., Meinzer, H., & Wolf, I. (2007) · Cited by 69

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Li, A., Zhang, X., Singla, J., White, K. L., Loconte, V., Hu, C., Zhang, C., Li, S., Li, W., Francis, J. P., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265567 · Cited by 11

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Li, L., Lian, S., Luo, Z., Li, S., Wang, B., & Li, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_25 · Cited by 11

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Rampun, A., Jarvis, D. A., Griffiths, P. D., Reyer Zwiggelaar, A., Scotney, B. W., & Armitage, P. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7100200 · Cited by 10

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Kern, D., & Mastmeyer, A. (2022) · Journal of Image and Graphics · doi:10.18178/joig.10.1.17-27 · Cited by 10

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Yuan, F., Tang, Z., Wang, C., Huang, Q., & Shi, J. (2023) · IET Image Processing · doi:10.1049/ipr2.12852 · Cited by 10

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Lee, S., Han, K., Shin, H., Park, H., Kim, S., Kim, J., Yang, X., Yang, J. D., Yu, H. C., & You, H. (2025) · Applied Sciences · doi:10.3390/app15020837 · Cited by 9

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Lu, Y., Zhao, Y., Chen, X., & Guo, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4103524 · Cited by 9

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Sadikine, A., Badic, B., Tasu, J., Noblet, V., Ballet, P., Visvikis, D., & Conze, P.-H. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102356 · Cited by 9

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Zhou, L., Wang, L., Li, W., Lei, B., Mi, J., & Yang, W. (2021) · IEEE Access · doi:10.1109/access.2021.3097387 · Cited by 8

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Zhang, L., Li, J., Li, P., Lu, X., Gong, M., Shen, P., Zhu, G., Shah, S. A. A., Bennamoun, M., Qian, K., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06750-9 · Cited by 8

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Bereska, J. I., Zeeuw, M. J., Wagenaar, L., Jenssen, H. B., Wesdorp, N. J., van der Meulen, D., Bereska, L., Gavves, E., Janssen, B. V., Besselink, M. G., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01820-7 · Cited by 9

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Barbera, G. L., Gori, P., Boussaid, H., Belucci, B., Delmonte, A., Goulin, J., Sarnacki, S., Rouet, L., & Isabelle Bloch, A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434090 · Cited by 7

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Arulappan, A., & Raj, T. A. B. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22663 · Cited by 7

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Shi, W., & Liu, H. (2019) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac47372.2019.8997642 · Cited by 7

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Li, D., Chen, C., Li, J., & Wang, L. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02138-7 · Cited by 7

Effect of input size on the classification of lung nodules using convolutional neural networks

Polat, G., Doğrusöz, Y. S., & Halıcı, U. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404659 · Cited by 7

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Yang, T., Yang, C., Sun, Y., & Horng, M. (2021) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-021-00615-1 · Cited by 6

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Złahoda-Huzior, A., Stanuch, M., Witowski, J. S., & Dudek, D. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857409 · Cited by 6

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Skwirczyński, M., Tabor, Z., Lasek, J., Schneider, Z., Gibała, S., Kucybała, I., Urbanik, A., & Obuchowicz, R. (2023) · Cancers · doi:10.3390/cancers15123142 · Cited by 6

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Tang, Y., Gao, R., Lee, H. H., Chen, Y., Gao, D., Bermudez, C. L., Bao, S., Huo, Y., Savoie, B. V., & Landman, B. A. (2021) · Medical Physics · doi:10.1002/mp.14706 · Cited by 6

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Li, C., Lu, Y., Yu, S. P., & Cui, Y. J. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103297 · Cited by 5

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Su, P., Shih, H.-J., & Xu, J.-L. (2025) · JMIR Medical Informatics · doi:10.2196/80351 · Cited by 5

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Zheng, S., Fang, B., Li, L., Gao, M., Zhang, H.-S., Chen, H., & Wang, Y. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950515 · Cited by 5

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Fernández, J. G., Fortunati, V., & Mehrkanoon, S. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533649 · Cited by 5

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Li, J., Liu, A., Li, Y., Wei, W., Qian, R., Xie, Q., Qiu, B., & Chen, X. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3313131 · Cited by 5

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Xiao, H., Wang, Y., Xiong, S., Ren, Y., & Zhang, H. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108755 · Cited by 5

SRCNN

Liu, S., Deng, Y., Xin, J., Zuo, W., Shi, P., & Zheng, N. (2018) · International Conference on Graphics and Signal Processing (ICGSP) · doi:10.1145/3282286.3282297 · Cited by 5

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de Sousa, P., Galdrán, A., Costa, P., & Campilho, A. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759309 · Cited by 5

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Li, G., Wang, K., Wang, X., Sun, B., Wang, K., Gao, Y., Sun, S., Kamiya, T., & Dai, Y. (2023) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3594315.3594337 · Cited by 5

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Pino, C., Palazzo, S., Trenta, F., Cordero, F., Bağcı, U., Rundo, F., Battiato, S., Giordano, D., Aldinucci, M., & Spampinato, C. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433832 · Cited by 5

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Polat, G., Halıcı, U., & Doğrusöz, Y. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01424 · Cited by 5

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Xin, M., Wen, J. A., Wang, Y., Yu, W. B., Fang, B., Hu, J., Xu, Y., & Linghu, C. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142157007x · Cited by 4

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Mourya, G. K., Bhatia, D., & Handique, A. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0690 · Cited by 4

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Liu, Z., Li, S., Chen, Y.-K., Liu, T., Liu, Q., Xu, X., Shi, Y., & Wen, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_40 · Cited by 4

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Ma, J., Chen, Y., Chen, Y., Wan, F., Xue, S., Li, Z., & Feng, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10346 · Cited by 4

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Xia, K., & Zhao-Yang, L. I. U. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525519 · Cited by 3

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Nguyen, M.-T., Kim, T. D., Ha, M.-H., Do, A.-L., Nguyen, L.-A., & Ngo, D.-L. (2023) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf60135.2023.10471798 · Cited by 3

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Li, Q., Tong, J., Yang, S., & Du, S. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10633071 · Cited by 3

Automatic liver segmentation in computed tomography scans using deep semantic segmentation

Ezzat, K. A., Omran, L. N., & Seddawy, A. I. B. E. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i1.4022 · Cited by 3

Progressive attention module for segmentation of volumetric medical images

Zhang, M., Pan, H., Zhu, Y., & Gu, Y. (2021) · Medical Physics · doi:10.1002/mp.15369 · Cited by 3

Parameter-Free Latent Space Transformer for Zero-Shot Bidirectional Cross-modality Liver Segmentation

Li, Y., Zou, B., Dai, Y., Zhu, C., Yang, F., Li, X., Bai, H. X., & Jiao, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_59 · Cited by 3

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Yang, M., Ye, Y., Ye, K., Zhou, W., Hu, X., & Hu, B. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02110-0 · Cited by 3

Pretreatment risk factors for overall survival in patients with gastrointestinal and non-gastrointestinal mucosa associated lymphoid tissue lymphomas.

Virijević, M., Peruničić-Jovanović, M., Đjunić, I., Novković, A., & Mihaljević, B. (2014) · PubMed · Cited by 3

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Kharrat, A., & Neji, M. (2021) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.20211001.oa4 · Cited by 3

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Huang, Y., Wen, J. A., Wang, Y., Hu, J., Wang, Y., & Yang, W. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-798 · Cited by 3

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Gao, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-13736-6_10 · Cited by 3

AgileNet: A Rapid and Efficient Breast Lesion Segmentation Method for Medical Image Analysis

Liang, J., Huang, T., Li, D., Ding, Z., Li, Y., Huang, L., Wang, Q., & Zhang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8469-5_33 · Cited by 2

Assessing the relevance of multi-planar MRI acquisitions for prostate segmentation using deep learning techniques

Patriti, S., Lozoya, R. C., Brag, J., Oubel, E., & Iannessi, A. (2018) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2293514 · Cited by 2

Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline

Xie, L., Wisse, L. E. M., Wang, J., Ravikumar, S., Glenn, T. W., Luther, A., Lim, S., Wolk, D. A., & Yushkevich, P. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_33 · Cited by 2

Global Context and Enhanced Feature Guided Residual Refinement Network for 3D Cardiovascular Image Segmentation

Liu, J., Wei, A., Guo, Z., & Tang, C. (2021) · IEEE Access · doi:10.1109/access.2021.3129333 · Cited by 2

Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation

Cao, P., Sheng, Q., Fang, S., Li, X., Ning, G., & Pan, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2984661 · Cited by 2

Wavelet J-Net: A Frequency Perspective on Convolutional Neural Networks

Zhang, L., Zhang, X., Bao, C., & Ma, K. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533652 · Cited by 2

CR-UNet: Context-rich UNet for liver segmentation from CT volumes

Zhang, J., Ji, B., Jiang, Z., & Qin, J. (2021) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs53707.2021.9588086 · Cited by 2

Automatic Liver Segmentation in CT Volumes with Improved 3D U-net

Liu, C., Cui, D., Shi, D., Hu, Z., Qin, Y. H., & Lang, J. (2018) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3285996.3286014 · Cited by 2

Liver Segmentation in CT Images with Adversarial Learning

Chen, Y., Li, S., Yang, S., & Luo, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26763-6_45 · Cited by 2

A Multi-scale Attention Network for Kidney Tumor Segmentation on CT Scans

Sun, L., Zeng, W., Ding, X., & Huang, Y. (2019) · Kidney Tumor Segmentation Challenge (KiTS) · doi:10.24926/548719.055 · Cited by 2

Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation.Preprint

Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E. K., & Yuille, A. (2017) · arXiv (Cornell University) · Cited by 2

Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography

Ye, S., Chen, C., Bai, Z., Wang, J., Yao, X., & Nedzvedz, O. (2022) · Sensors · doi:10.3390/s22145171 · Cited by 1

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Yang, D., Xu, D., Zhou, S. K., Georgescu, B., Chen, M., Grbić, S., Metaxas, D., & Comaniciu, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_58 · Cited by 233

Automatic 3D liver location and segmentation via convolutional neural networks and graph cutPreprint

Lu, F., Wu, F., Hu, P., Peng, Z., & Kong, D.-X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.03012 · Cited by 10

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Zeng, Y.-Z., Duan, Z.-C., Di, S., Yang, Z., Zhao, Y., Wu, W. H., Chen, Z., & Chun-Liang, Z. (2023) · International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT) · doi:10.1109/mvipit60427.2023.00034 · Cited by 1

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Lu, F., Wu, F., Hu, P., Peng, Z., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1467-3 · Cited by 284

Automatic Segmentation and 3D Reconstruction of Liver and Tumor

Wardhana, G. (2018)