RCI-Seg: Robust click-based interactive segmentation framework with deep reinforcement learning for biomedical images

U-Net: Convolutional Networks for Biomedical Image Segmentation

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Human-level control through deep reinforcement learning

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UNet++: A Nested U-Net Architecture for Medical Image Segmentation

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GrabCut: Interactive foreground extraction using iterated graph cuts

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Attention U-Net: Learning Where to Look for the Pancreas

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Deep Reinforcement Learning with Double Q-Learning

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Random Walks for Image Segmentation

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Graph Cuts and Efficient N-D Image Segmentation

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Panoptic Segmentation

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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

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The Liver Tumor Segmentation Benchmark (LiTS)

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Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Janowczyk, A. R., & Madabhushi, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186902 · Cited by 1,355 · see citing works

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2677499 · Cited by 1,136 · see citing works

Gland segmentation in colon histology images: The glas challenge contest

Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.08.008 · Cited by 989 · see citing works

Interactive medical image segmentation using deep learning with image-specific fine tuning

Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843 · see citing works

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

Naylor, P., Lae, M. E., Reyal, F., & Walter, T. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2865709 · Cited by 688 · see citing works

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen, H., Qi, X., Yu, L., & Heng, P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.273 · Cited by 671 · see citing works

ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images

Yushkevich, P. A., Gao, Y., & Gerig, G. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591443 · Cited by 651 · see citing works

A Multi-Organ Nucleus Segmentation Challenge

Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P., Li, J., Hu, Z., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2947628 · Cited by 559 · see citing works

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works

Deep Extreme Cut: From Extreme Points to Object Segmentation

Maninis, K.-K., Caelles, S., Pont-Tuset, J., & Gool, L. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00071 · Cited by 465 · see citing works

Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

Seo, H., Huang, C. W., Bassenne, M., Xiao, R., & Xing, L. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2948320 · Cited by 433 · see citing works

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

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VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

Sekuboyina, A., Husseini, M. E., Bayat, A., Löffler, M. T., Liebl, H., Li, H., Tetteh, G., Kukačka, J., Payer, C., Štern, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102166 · Cited by 391 · see citing works

Annotating Object Instances with a Polygon-RNN

Castrejón, L., Kundu, K., Urtasun, R., & Fidler, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.477 · Cited by 371 · see citing works

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Mahmood, F., Borders, D., Chen, R. J., McKay, G. N., Salimian, K. J., Baras, A. S., & Durr, N. J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927182 · Cited by 347 · see citing works

GeoS: Geodesic Image Segmentation

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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

Sun, C., Guo, S., Zhang, H., Li, J., Chen, M., Shu-zhi, M., Jin, L., Liu, X., Li, X., & Qian, X. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.03.008 · Cited by 237 · see citing works

CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

Zhou, Y., Onder, O. F., Dou, Q., Tsougenis, E., Chen, H., & Heng, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_53 · Cited by 229 · see citing works

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

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3D Segmentation in the Clinic: A Grand Challenge II: MS lesion segmentation

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NuClick: A deep learning framework for interactive segmentation of microscopic images

Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101771 · Cited by 147 · see citing works

Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation

Zhao, B., Chen, X., Li, Z., Yu, Z., Yao, S., Yan, L., Wang, Y., Liu, Z., Liang, C., & Han, C. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101786 · Cited by 144 · see citing works

Interactive Object Segmentation With Inside-Outside Guidance

Zhang, S., Liew, J. H., Wei, Y., Wei, S., & Zhao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01225 · Cited by 142 · see citing works

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102102 · Cited by 126 · see citing works

Iteratively-Refined Interactive 3D Medical Image Segmentation With Multi-Agent Reinforcement Learning

Liao, X., Li, W., Xu, Q., Wang, X., Jin, B., Zhang, X., Wang, Y., & Zhang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00941 · Cited by 113 · see citing works

Volumetric memory network for interactive medical image segmentation

Zhou, T., Li, L., Bredell, G., Li, J., Unkelbach, J., & Konukoglu, E. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102599 · Cited by 109 · see citing works

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Interactive medical image annotation using improved Attention U-net with compound geodesic distance

Zhang, Y., Chen, J., Ma, X., Wang, G., Bhatti, U. A., & Huang, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121282 · Cited by 81 · see citing works

Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views

Wang, G., Zuluaga, M. A., Pratt, R., Aertsen, M., Doel, T., Klusmann, M., David, A. L., Deprest, J., Vercauteren, T., & Ourselin, S. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.04.009 · Cited by 73 · see citing works

Interactive Full Image Segmentation by Considering All Regions Jointly

Agustsson, E., Uijlings, J., & Ferrari, V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01189 · Cited by 71 · see citing works

FocusCut: Diving into a Focus View in Interactive Segmentation

Zheng, L. L., Duan, Z.-P., Zhang, Z., Guo, C., & Cheng, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00266 · Cited by 70 · see citing works

Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

Qadri, S. F., Ai, D., Hu, G., Ahmad, M., Huang, Y., Wang, Y., & Yang, J. (2018) · Applied Sciences · doi:10.3390/app9010069 · Cited by 67 · see citing works

E$$^2$$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

Tang, Y., Tang, Y., Zhu, Y., Xiao, J., & Summers, R. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_50 · Cited by 65 · see citing works

2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT

Lv, P., Wang, J., & Wang, H. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103567 · Cited by 59 · see citing works

A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach

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FedDP: Dual Personalization in Federated Medical Image Segmentation

Wang, J., Jin, Y., Stoyanov, D. V., & Wang, L. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3299206 · Cited by 53 · see citing works

Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework

Lou, W., Li, H., Li, G., Han, X., & Wan, X. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3221666 · Cited by 47 · see citing works

Meta multi-task nuclei segmentation with fewer training samples

Han, C., Yao, H., Zhao, B., Li, Z., Shi, Z., Wu, L., Chen, X., Qu, J., Zhao, K., Lan, R., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102481 · Cited by 47 · see citing works

Interactive medical image segmentation via a point-based interaction

Zhang, J., Shi, Y., Sun, J., Wang, L., Zhou, L., Gao, Y., & Shen, D. G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101998 · Cited by 46 · see citing works

Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field

Chen, Y., Zheng, C., Hu, F., Zhou, T., Feng, L., Guohui, X., Yi, Z., & Zhang, X. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106076 · Cited by 46 · see citing works

Robust Hand Gesture Recognition Based on RGB-D Data for Natural Human–Computer Interaction

Xu, J., Wang, H., Zhang, J., & Cai, L. (2022) · IEEE Access · doi:10.1109/access.2022.3176717 · Cited by 45 · see citing works

3D MRU-Net: A novel mobile residual U-Net deep learning model for spine segmentation using computed tomography images

Saeed, M. U., Wang, B., Sheng, J., Ali, G., & Dastgir, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105153 · Cited by 45 · see citing works

Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts

Yang, Z., Zhao, Y., Liao, M., Di, S., & Zeng, Y.-Z. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102670 · Cited by 39 · see citing works

DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation

Zhang, Y., Peng, C., Peng, L., Xu, Y., Lin, L., Tong, R., Peng, Z., Mao, X., Hu, H., Chen, Y., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3091900 · Cited by 36 · see citing works

Interactive-cut: Real-time feedback segmentation for translational research

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Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input

Jahanifar, M., Tajeddin, N. Z., Koohbanani, N. A., & Rajpoot, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00081 · Cited by 26 · see citing works

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks

Zareie, M., Parsaei, H., Amiri, S., Awan, M. S. K., & Ghofrani-Jahromi, M. (2018) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-018-0702-3 · Cited by 18 · see citing works

A deep learning-based interactive medical image segmentation framework with sequential memory

Mikhailov, I., Chauveau, B., Bourdel, N., & Bartoli, A. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108038 · Cited by 15 · see citing works

RAU-Net: U-Net network based on residual multi-scale fusion and attention skip layer for overall spine segmentation

Yang, Z., Wang, Q., Zeng, J., Qin, P., Chai, R., & Sun, D. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01360-4 · Cited by 14 · see citing works

TMPSformer: An Efficient Hybrid Transformer-MLP Network for Polyp Segmentation

Guo, P., Liu, G., & Liu, H. (2024) · Mobile Networks and Applications · doi:10.1007/s11036-024-02411-y · Cited by 6 · see citing works

Speeding up omnidirectional video quality assessment with reinforcement learning based key-frame selection

Yu, R., & Hu, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131834

Reinforcement Learning Approach to Instance Segmentation Using Double DQN

Denknalbant, A., Ahangari, M., Kaya, B., Saidburkhan, A., & Arıcan, E. (2025) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu67174.2025.11208352

FUSE-RAG: Few-Shot Universal Segmentation with Retrieval-Augmented Generation for Medical Imaging

Allaoui, M. L., Allili, M. S., & Belaid, A. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3807452

Adaptive reinforcement learning for medical image classification

Chaddad, A., Jiang, Y., Daqqaq, T. S., & Kateb, R. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124003

FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning

Qian, C., Yang, H., & Li, J. (2023) · 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc53992.2023.10393973 · Cited by 1 · see citing works

Interactive medical image segmentation via a point-based interaction

Zhang, J., Shi, Y., Sun, J., Wang, L., Zhou, L., Gao, Y., & Shen, D. G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101998 · Cited by 46 · see citing works

Progressive medical image annotation with convolutional neural network-based interactive segmentation method

Bai, Y., Sun, G., Li, Y., Shen, L., & Zhang, L. (2021) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2582241 · Cited by 2 · see citing works

IMedSeg: Towards efficient interactive medical segmentation

Shi, Z., Chen, T., Zhou, Q., Zou, H., & Xiao, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129419 · Cited by 6 · see citing works

IKAN: Interactive KAN with Modulation Fusion for Medical Image Segmentation

Liu, S., Wan, T., Cai, Y., Chen, S., Hu, B., Wan, Y., & Qiu, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_26