MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning
Open accessAbstract
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. However, traditional interactive segmentation methods require a large number of user interactions, and recently proposed CNN-based interactive segmentation methods are limited by poor performance on previously unseen objects. To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects. Specifically, we first encode user-provided interior margin points via our proposed exponentialized geodesic distance that enables a CNN to achieve a good initial segmentation result of both previously seen and unseen objects, then we use a novel information fusion method that combines the initial segmentation with only a few additional user clicks to efficiently obtain a refined segmentation. We validated our proposed framework through extensive experiments on 2D and 3D medical image segmentation tasks with a wide range of previously unseen objects that were not present in the training set. Experimental results showed that our proposed framework 1) achieves accurate results with fewer user interactions and less time compared with state-of-the-art interactive frameworks and 2) generalizes well to previously unseen objects.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,953 · see citing works
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901 · see citing works
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · cited 10× · see citing works
“Two users used MIDeepSeg and two existing methods with good generalizability including ITK-SNAP (Yushkevich et al., 2006) and 3D versions of Graph Cuts (Boykov and Jolly, 2001), DIOS (Xu et al.”
BackgroundMethodInfluential3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Boykov, Y. Y., & Jolly, M.-P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937505 · Cited by 3,674 · see citing works
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works
Random Walks for Image Segmentation
Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624 · see citing works
Bottom-Up Object Detection by Grouping Extreme and Center Points
Zhou, X., Zhuo, J., & Krahenbuhl, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00094 · Cited by 1,088 · 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
Self-citationDeepIGeoS: 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 · cited 21× · see citing works
“Following DeepIGeoS (Wang et al., 2018b), we used open source code to compute geodesic distance 1 and solve Eq.”
BackgroundMethodInfluentialSelf-citation3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationPreprint
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06650 · Cited by 488 · 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
Deep Interactive Object Selection
Xu, N., Price, B., Cohen, S., Yang, J., & Huang, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.47 · Cited by 460 · see citing works
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
Acuna, D., Ling, H., Kar, A., & Fidler, S. (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.2018.00096 · Cited by 436 · see citing works
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2621185 · Cited by 410 · 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
DeepMedic for Brain Tumor Segmentation
Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., & Glocker, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-55524-9_14 · Cited by 346 · see citing works
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting
Xue, B., & Sapiro, G. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0191-z · Cited by 300 · see citing works
GeoS: Geodesic Image Segmentation
Criminisi, A., Sharp, T., & Blake, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_9 · Cited by 277 · see citing works
Large-Scale Interactive Object Segmentation With Human Annotators
Benenson, R., Popov, S., & 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.2019.01197 · Cited by 243 · see citing works
Interactive Image Segmentation with Latent Diversity
Li, Z., Chen, Q., & Koltun, 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.2018.00067 · Cited by 218 · see citing works
A Survey on Medical Image Segmentation
Masood, S., Sharif, M., Masood, A., Yasmin, M., & Raza, M. (2015) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/157340561101150423103441 · Cited by 190 · see citing works
Geodesic graph cut for interactive image segmentation
Price, B. L., Morse, B. S., & Cohen, S. D. (2010) · 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.2010.5540079 · Cited by 186 · see citing works
Deep GrabCut for Object Selection
Xu, N., Price, B. L., Cohen, S. D., Yang, J., & Huang, T. S. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.182 · Cited by 149 · see citing works
(2013) · Liverpool John Moores University · Cited by 133 · see citing works
Active Learning for Interactive 3D Image Segmentation
Top, A., Hamarneh, G., & Rafeef Abugharbieh, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_74 · Cited by 121 · see citing works
A fully convolutional two-stream fusion network for interactive image segmentation
Hu, Y., Soltoggio, A., Lock, R., & Carter, S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.10.009 · Cited by 113 · see citing works
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
Bottom-up Object Detection by Grouping Extreme and Center PointsPreprint
Zhou, X., Zhuo, J., & Krähenbühl, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08043 · Cited by 105 · see citing works
Object Instance Annotation With Deep Extreme Level Set Evolution
Wang, Z., Acuna, D., Ling, H., Kar, A., & Fidler, S. (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.2019.00768 · Cited by 83 · see citing works
Lee, K. M., Myeong, H., & Song, G. (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.2018.00189 · Cited by 74 · see citing works
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
Self-citationGoing to Extremes: Weakly Supervised Medical Image Segmentation
Roth, H. R., Yang, D., Xu, Z., Wang, X., & Xu, D. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3020026 · Cited by 67 · see citing works
Interactive segmentation of medical images through fully convolutional neural networksPreprint
Sakinis, T., Milletarì, F., Roth, H. R., Korfiatis, P. D., Kostandy, P., Philbrick, K. A., Akkus, Z., Xu, Z., Xu, D., & Erickson, B. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08205 · Cited by 59 · see citing works
Intelligent labeling based on fisher information for medical image segmentation using deep learning
Sourati, J., Gholipour, A., Dy, J. G., Tomas-Fernandez, X., Kurugol, S., & Warfield, S. K. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907805 · Cited by 50 · see citing works
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input
Liew, J. H., Cohen, S. D., Price, B. L., Mai, L., Ong, S., & Feng, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00075 · Cited by 38 · see citing works
Wang, G., Aertsen, M., Deprest, J. A., Sebastien Ourselin, A., Vercauteren, T. K., & Zhang, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_28 · Cited by 37 · see citing works
Self-citationExtreme Clicking for Efficient Object Annotation
Papadopoulos, D. P., Uijlings, J., Keller, F., & Ferrari, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.528 · Cited by 34 · see citing works
Guide Me: Interacting with Deep Networks
Rupprecht, C., Laina, I., Navab, N., Hager, G. D., & Tombari, F. (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.2018.00892 · Cited by 26 · see citing works
4D active cut: An interactive tool for pathological anatomy modeling
Wang, B., Liu, K., Prastawa, M. W., Irima, A., Vespa, P. M., Horn, J. D. V., Fletcher, P. T., & Gerig, G. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6867925 · Cited by 23 · see citing works
Weakly Supervised Segmentation from Extreme Points
Roth, H. R., Zhang, L., Yang, D., Milletarì, F., Xu, Z., Wang, X., & Xu, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33642-4_5 · Cited by 19 · see citing works
Zhou, L., Heller, N., Shi, Y., Xiao, Y., Sznitman, R., Cheplygina, V., Mateus, D., Trucco, E., Hu, X. S., Chen, D., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33642-4 · Cited by 19 · see citing works
Khan, S., Shahin, A. H., Villafruela, J., Shen, J., & Shao, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_8 · Cited by 18 · see citing works
Interactive Deep Editing Framework for Medical Image Segmentation
Zhou, B., Chen, L., & Zhao, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_37 · Cited by 16 · see citing works
Marsh, H. (2013) · doi:10.17077/etd.hbxcmbbg · Cited by 15 · see citing works
Dynamically Balanced Online Random Forests for Interactive Scribble-Based Segmentation
Wang, G., Zuluaga, M. A., Pratt, R., Aertsen, M., Doel, T., Klusmann, M., David, A. L., Deprest, J., Vercauteren, T., & Ourselin, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_41 · Cited by 14 · see citing works
Self-citationDeep GrabCut for Object SelectionPreprint
Xu, N., Price, B. L., Cohen, S. D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00243 · Cited by 14 · see citing works
Raju, A., Ji, Z., Cheng, C., Cai, J., Huang, J., Xiao, J. Z., Lü, L., Liao, C., & Harrison, A. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59710-8_45 · Cited by 13 · see citing works
IEEE International Symposium on Biomedical Imaging
(2015) · IEEE Pulse · doi:10.1109/mpul.2015.2417197 · Cited by 13 · see citing works
Data Augmentation Based on Substituting Regional MRIs Volume Scores
Leng, T., Zhao, Q., Yang, C., Lu, Z., Adeli, E., & Pohl, K. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33642-4_4 · Cited by 4 · see citing works
Going to Extremes: Weakly Supervised Medical Image SegmentationPreprint
Roth, H. R., Yang, D., Xu, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.11988 · Cited by 4 · see citing works
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input
Hao, L. J., Scott, C., Brian, P., et al. (2019) · IEEE Conference Proceedings · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913 · see citing works
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
Luo, X., Wang, G., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Metaxas, D., & Zhang, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102517 · Cited by 419 · see citing works
Self-citationLABKIT: Labeling and Segmentation Toolkit for Big Image Data
Arzt, M., Deschamps, J. R., Schmied, C., Pietzsch, T., Schmidt, D., Tomančák, P., Haase, R., & Jug, F. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.777728 · Cited by 352 · see citing works
Luo, X., Liao, W., Xiao, J., Chen, J., Song, T., Zhang, X., Li, K., Metaxas, D., Wang, G., & Zhang, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102642 · Cited by 193 · see citing works
Self-citationSAM-Med3D: Towards General-Purpose Segmentation Models for Volumetric Medical Images
Wang, H., Guo, S., Jin, Y., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91721-9_4 · Cited by 173 · see citing works
Self-citationMedical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods
Gao, Y., Jiang, Y., Peng, Y., Yuan, F., Zhang, X., & Wang, J. (2025) · Tomography · doi:10.3390/tomography11050052 · Cited by 172 · see citing works
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image
Ning, Z., Zhong, S., Feng, Q., Chen, W., & Zhang, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116087 · Cited by 171 · see citing works
MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images
Diaz‐Pinto, A., Alle, S., Nath, V., Tang, Y., Ihsani, A., Asad, M., Pérez‐García, F., Mehta, P., Li, W., Flores, M. G., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103207 · Cited by 171 · see citing works
Self-citation3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.-A., & Dou, Q. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103324 · Cited by 156 · see citing works
Self-citationA comprehensive survey on deep active learning in medical image analysis
Wang, H., Jin, Q., Li, S., Liu, S., Wang, M., & Song, Z. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103201 · Cited by 139 · see citing works
Luo, X., Hu, M., Liao, W., Zhai, S., Song, T., Wang, G., & Zhang, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_50 · Cited by 134 · see citing works
Self-citationSAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.15161 · Cited by 115
Self-citationVolumetric 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
Challenges for machine learning in clinical translation of big data imaging studies
Dinsdale, N. K., Bluemke, E., Sundaresan, V., Jenkinson, M., Smith, S. M., & Namburete, A. I. L. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.012 · Cited by 69 · see citing works
Segment Anything in Medical Images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2023) · arXiv · doi:10.48550/arxiv.2304.12306 · Cited by 67 · see citing works
SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3586694 · Cited by 67 · see citing works
Self-citationGao, F., Hu, M., Zhong, M., Feng, S., Tian, X., Meng, X., Nijiati, M., Huang, Z.-P., Lv, M., Song, T., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102515 · Cited by 62 · see citing works
Self-citationReviewing 3D convolutional neural network approaches for medical image segmentation
Ilesanmi, A. E., Ilesanmi, T. O., & Ajayi, B. O. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27398 · Cited by 59 · see citing works
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image
Wong, H. E., Rakic, M., Guttag, J. V., & Dalca, A. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_12 · Cited by 59 · see citing works
Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction
Han, J., Liu, Y., Li, Z., Liu, Y., & Zhan, B. (2023) · Sensors · doi:10.3390/s23041822 · Cited by 54 · see citing works
Zhai, S., Wang, G., Luo, X., Yue, Q., Li, K., & Zhang, S. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3245068 · Cited by 49 · see citing works
Self-citationSATS: Self-attention transfer for continual semantic segmentation
Qiu, Y., Shen, Y., Sun, Z., Zheng, Y., Chang, X., Zheng, W., & Wang, R. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109383 · Cited by 47 · see citing works
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Marinov, Z., Jäger, P. F., Egger, J., Kleesiek, J., & Stiefelhagen, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3452629 · Cited by 47 · see citing works
Luo, X., Fu, J. C., Zhong, Y., Liu, S., Han, B., Astaraki, M., Bendazzoli, S., Toma-Daşu, I., Ye, Y., Chen, Z., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103447 · Cited by 46 · see citing works
Self-citationiSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images
Liu, Q., Xu, Z., Jiao, Y., & Niethammer, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_45 · Cited by 45 · see citing works
Upadhyay, A. K., & Bhandari, A. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10028-9 · Cited by 43 · see citing works
Efficient Click-Based Interactive Segmentation for Medical Image With Improved Plain-ViT
Huang, M., Zou, J., Zhang, Y., Bhatti, U. A., & Chen, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392893 · Cited by 37 · see citing works
Li, X., Xia, M., Jiao, J., Zhou, S., Chang, C., Wang, Y., & Guo, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102862 · Cited by 35 · see citing works
Li, N., Xiong, L., Qiu, W., Pan, Y., Luo, Y., & Zhang, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8141-0_11 · Cited by 28 · see citing works
Zhu, J., Bolsterlee, B., Chow, B. V. Y., Song, Y., & Meijering, E. H. W. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102383 · Cited by 28 · see citing works
Ramaekers, M., Viviers, C. G. A., Janssen, B. V., Hellström, T. A. E., Ewals, L. J. S., van der Wulp, K., Nederend, J., Jacobs, I., Pluyter, J. R., Mavroeidis, D., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134209 · Cited by 26 · see citing works
SaltISNet3D: Interactive Salt Segmentation from 3D Seismic Images Using Deep Learning
Zhang, H., Zhu, P., & Liao, Z. (2023) · Remote Sensing · doi:10.3390/rs15092319 · Cited by 24 · see citing works
Diversity-Promoting Ensemble for Medical Image Segmentation
Georgescu, M.-I., Ionescu, R. T., & Miron, A. I. (2023) · ACM Symposium on Applied Computing · doi:10.1145/3555776.3577682 · Cited by 24 · see citing works
Yuan, S., Qin, H., Kou, R., Yan, X., Li, Z., Peng, C., Wu, D., & Zhou, H. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3545014 · Cited by 21 · see citing works
Segmentation of hard exudate lesions in color fundus image using two-stage CNN-based methods
Do, Q. V., Hoang, H. T., Vu, N., Jesus, D. A. D., Brea, L. S., Nguyen, H. X., Nguyen, A. T. L., Le, T. N., Dinh, D. T. M., Nguyen, M. T. B., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122742 · Cited by 21 · see citing works
Fu, J., Wang, G., Lu, T., Yue, Q., Vercauteren, T. K., Sebastien Ourselin, A., & Zhang, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111204 · Cited by 21 · see citing works
Self-citationLearning label diffusion maps for semi-automatic segmentation of lung CT images with COVID-19
Bruzadin, A., Boaventura, M., Colnago, M., Negri, R. G., & Casaca, W. C. D. O. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.12.003 · Cited by 17 · see citing works
HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations
Schira, M. M., Isherwood, Z. J., Kassem, M. S., Barth, M., Shaw, T. B., Roberts, M. M., & Paxinos, G. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02653-8 · Cited by 17 · see citing works
Attention‐enhanced multiscale feature fusion network for pancreas and tumor segmentation
Dong, K., Hu, P., Zhu, Y., Tian, Y., Li, X., Zhou, T., Bai, X., Liang, T., & Li, J. (2024) · Medical Physics · doi:10.1002/mp.17385 · Cited by 17 · see citing works
A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions
Gan, H., Ramlee, M. H., Wang, Z., & Shimizu, A. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1574 · Cited by 17 · see citing works
A Review of Deep Learning‐Based Medical Image Segmentation
Zhang, X., Wang, J., Cheng, X., & Li, J. (2025) · IET Image Processing · doi:10.1049/ipr2.70163 · Cited by 16 · see citing works
Das, S., Dubey, R., Jena, B., Tsai, L.-W., & Saxena, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18336-3 · Cited by 14 · see citing works
Atzeni, A., Peter, L., Robinson, E. D., Blackburn, E., Althonayan, J., Alexander, D. C., & Iglesias, J. (2022) · Medical Image Anal. · doi:10.1016/j.media.2022.102549 · Cited by 20 · see citing works
Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model
Zambrano, M., V, Botto-Tobar, M., Suárez, C. I. H., Paredes-Parada, W., Patiño-Pérez, D., Ahanger, T. A., & Benítez, N. G. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6872045 · Cited by 14 · see citing works
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
Hallitschke, V. J., Schlumberger, T., Kataliakos, P., Marinov, Z., Kim, M., Heiliger, L., Seibold, C., Kleesiek, J., & Stiefelhagen, R. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230334 · Cited by 14 · see citing works
SenseCare: a research platform for medical image informatics and interactive 3D visualization
Wang, G., Duan, Q., Tian, S., & Zhang, S. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1460889 · Cited by 13 · see citing works
Self-citationPRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Li, H., Liu, H., Hu, D., Wang, J., & Oguz, I. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72384-1_37 · Cited by 12 · see citing works
Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning
Spaanderman, D. J., Starmans, M. P. A., van Erp, G. C. M., Hanff, D. F., Sluijter, J. H., Schut, A. W., van Leenders, G. J. L. H., Verhoef, C. K., Grünhagen, D. J., Niessen, W. J., et al. (2024) · European Radiology · doi:10.1007/s00330-024-11167-8 · Cited by 12 · see citing works
Weakly supervised segmentation of uterus by scribble labeling on endometrial cancer MR images
Ying, J., Huang, W., Fu, L., Yang, H., & Cheng, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107582 · Cited by 12 · see citing works
PixelSeg: Pixel-by-Pixel Stochastic Semantic Segmentation for Ambiguous Medical Images
Zhang, W., Zhang, X., Huang, S., Lü, Y., & Wang, K. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548060 · Cited by 10 · see citing works
Zhuang, M., Chen, Z., Wang, H., Tang, H., He, J., Qin, B., Yang, Y., Jin, X., Yu, M., Jin, B., et al. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02730-z · Cited by 10 · see citing works
Fu, J., Lu, T., Zhang, S., & Wang, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43990-2_30 · Cited by 9 · see citing works
Self-citationMarinov, Z., Stiefelhagen, R., & Kleesiek, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_61 · Cited by 8 · see citing works
Gutiérrez, J. D., Delgado, E., Carrasco, C. B., Conejero, J. M., & Rodríguez-Echeverría, R. (2025) · Algorithms · doi:10.3390/a18040227 · Cited by 7 · see citing works
Transforming the Interactive Segmentation for Medical Imaging
Liu, W., Ma, C., Yang, Y., Xie, W., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_67 · Cited by 7 · see citing works
Self-Supervised Interactive Image Segmentation
Shi, Q.-X., Li, Y., Di, H., & Wu, E. (2024) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2023.3295062 · Cited by 11 · see citing works
Liu, L.-W., Huang, Z. L., Lu, K.-Y., Wang, Z., Liang, Y., Lin, S.-Y., & Ji, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3369678 · Cited by 7 · see citing works
Zhuang, M., Chen, Z., Yang, Y., Kettunen, L., & Wang, H. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-02931-0 · Cited by 7 · see citing works
Le, Q. A., Pham, X. L., van Walsum, T., Dao, H. V., Le, T. L., Franklin, D. R., Moelker, A. D., Le, H. V., Trung, N. L., & Luu, H. M. (2024) · Medical Physics · doi:10.1002/mp.17373 · Cited by 6 · see citing works
CAISeg: A Clustering-Aided Interactive Network for Lesion Segmentation in 3D Medical Imaging
Sun, Y., Zhang, S., Li, J., Han, Q., & Qin, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3467279 · Cited by 6 · see citing works
Self-citationChang, A., Xing, T., Huang, Y., Yang, X., Zeng, J., Zhou, X., Huang, R., & Ni, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106973 · Cited by 6 · see citing works
DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images
Ning, Z., Mao, Y., Xu, X., Feng, Q., Zhong, S., & Zhang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110355 · Cited by 6 · see citing works
PE-MT: A Perturbation-Enhanced Mean Teacher for Semi-Supervised Image Segmentation
Wang, W., Li, Z., Zhang, X., Jiang, G., Wu, Y., Yu, S., Tian, B., Hu, M., Xu, X., Wu, W., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12050453 · Cited by 6 · see citing works
Zhang, X., Zhang, J., Zhang, Y., Jia, M., Li, H., & Liu, X. (2023) · IET Image Processing · doi:10.1049/ipr2.12865 · Cited by 6 · see citing works
Self-citationLASNet: A Light-Weight Asymmetric Spatial Feature Network for Real-Time Semantic Segmentation
Chen, Y., Zhan, W., Jiang, Y., Zhu, D., Guo, R., & Xu, X. (2022) · Electronics · doi:10.3390/electronics11193238 · Cited by 6 · see citing works
Aresta, G., Araújo, T., Fazekas, B., Mai, J., Schmidt-Erfurth, U., & Bogunović, H. (2024) · IEEE Access · doi:10.1109/access.2024.3379015 · Cited by 5 · see citing works
Visual prompt-driven universal model for medical image segmentation in radiotherapy
Zhu, S., Yu, C., Zhang, Y., & Hu, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114006 · Cited by 5 · see citing works
Zheng, Z., Luo, X., Pei-wen, L., Piao, S., Cao, X., Liu, X., Yang, L., Hu, B., Geng, Y., & Geng, D. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102624 · Cited by 5 · see citing works
Self-citationMDT: semi-supervised medical image segmentation with mixup-decoupling training
Long, J., Ren, Y. X., Yang, C., Ren, P., & Zeng, Z. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2715 · Cited by 5 · see citing works
A Two-Stage U-Net Framework for Interactive Segmentation of Lung Nodules in CT Scans
Fernandes, L., Pereira, T., & Oliveira, H. P. (2025) · IEEE Access · doi:10.1109/access.2025.3566203 · Cited by 5 · see citing works
Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation
Nie, X., Liu, L., He, L., Zhao, L., Lu, H., Lou, S., Xiong, R., & Wang, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3268157 · Cited by 5 · see citing works
Adaptive Multi-scale Online Likelihood Network for AI-Assisted Interactive Segmentation
Asad, M., Williams, H., Mandal, I., Ather, S., Deprest, J. A., D’hooge, J., & Vercauteren, T. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_53 · Cited by 5 · see citing works
Self-citationTian, Z., He, Y., Sun, L., Li, Y., & Du, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128184 · Cited by 5 · see citing works
WORD: Revisiting Organs Segmentation in the Whole Abdominal Region.Preprint
Luo, X., Liao, W., Xiao, J., Song, T., Zhang, X., Li, K., Wang, G., & Zhang, S. (2021) · arXiv (Cornell University) · Cited by 5 · see citing works
Self-citationLiu, K.-H., Wang, M., & Liu, T.-J. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342926 · Cited by 4 · see citing works
Specific Instance and Cross-Prompt-Based Robust 3-D Semi-Supervised Medical Image Segmentation
Zhou, Q., Feng, Y., Huang, Z., Ding, M., & Zhang, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3436124 · Cited by 4 · see citing works
An efficient interactive segmentation framework for medical images without pre‐training
Sun, L., Tian, Z., Chen, Z., Luo, W., & Du, S. (2022) · Medical Physics · doi:10.1002/mp.16120 · Cited by 4 · see citing works
Kou, W., Rey, C., Marshall, H., & Chiu, B. C. Y. (2024) · Bioengineering · doi:10.3390/bioengineering11080796 · Cited by 3 · see citing works
He, Q., Tan, H., Xiao, B., Tan, Y., Peng, X., Peng, C., Yue, X., Jiang, L., Cao, Y., Lv, F. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02232-7 · Cited by 3 · see citing works
Source-free cross-modality medical image synthesis with diffusion priors
Chen, J., Wang, X., Bai, J., Yang, K. G., Hu, X., & Li, Y. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00200-5 · Cited by 3 · see citing works
Nested star-shaped objects segmentation using diameter annotations
Camarasa, R., Kervadec, H., Kooi, M. E., Hendrikse, J., Nederkoorn, P. J., Bos, D., & de Bruijne, M. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102934 · Cited by 3 · see citing works
Memory-augmented Variational Adaptation for Online Few-shot Segmentation
Liu, J., Du, Y., Xiao, Z., Snoek, C. G. M., Sonke, J., & Gavves, E. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00357 · Cited by 3 · see citing works
Marinov, Z., Jaus, A., Kleesiek, J., & Stiefelhagen, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81854-7_3 · Cited by 3 · see citing works
He, Q., Tan, H., Xiao, B., Peng, X., Peng, C., Tan, Y., Liu, Y., Cao, Y., Lv, F. J., Zhao, W., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01415-z · Cited by 2 · see citing works
Pixel Diffuser: Practical Interactive Medical Image Segmentation without Ground Truth
Ju, M., Yang, J.-W., Lee, J.-Y., Lee, M., Ji, J., & Kim, Y. (2023) · Bioengineering · doi:10.3390/bioengineering10111280 · Cited by 2 · see citing works
ClickAttention: Click region similarity guided interactive segmentation
Xu, L., Chen, Y., Li, S., Chen, J., & Tang, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108090 · Cited by 2 · see citing works
Xu, X., Lu, W., Lei, J., Qiu, P., Shen, H., & Yang, Y. (2023) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai59581.2023.00062 · Cited by 2 · see citing works
Malagar, V., Upadhyay, N. M., & Sharma, M. (2025) · Wiley eBooks · doi:10.1002/9781394245369.ch11 · Cited by 2 · see citing works
Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery
Li, Z., Deng, Y., Ye, Q., Yu, W., Qi, H., Liu, Y., Yu, Z., & Bian, G.-B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610315 · Cited by 1 · see citing works
Murugadoss, R., Praveen, R., Kunjumohamad, S. C., & Baiju, P. S. (2025) · European Spine Journal · doi:10.1007/s00586-025-09542-0 · Cited by 1 · see citing works
LRD-3DSAM: A Novel SAM-Based 3D Medical Segmentation Network for Modeling Long-Range Dependencies
Zhang, W., Hu, Z., Wang, W., & Wang, X. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356547 · Cited by 1 · see citing works
Cooperative morphology-boundary network for accurate prostate segmentation from 3D MR images
Deng, Y., Song, T., Wang, X., Chen, Y., He, Q., & Huang, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109918 · Cited by 1 · see citing works
Self-citationDevi, M. S. D., Smitha, S. P., Josephine, R., Soumya, & Borah, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44430-2.00020-7 · Cited by 1 · see citing works
Luo, Z., & Wang, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87009-5_11 · Cited by 1 · see citing works
Self-citationHan, S.-C., Oh, J. K., Cho, W., Kim, T. J., Hong, N., & Park, S. B. (2025) · Journal of Korean Neurosurgical Society · doi:10.3340/jkns.2025.0033 · Cited by 1 · see citing works
Reservoir-enhanced segment anything model for subsurface diagnosis
Zhou, X., Liu, S., Yan, X., Fan, Y., Wang, X., Kang, Y., Cheng, J., & Chen, H. (2025) · Nature Communications · doi:10.1038/s41467-025-67382-4 · Cited by 1 · see citing works
PIMedSeg: Progressive interactive medical image segmentation
Gong, X., Wang, L., Miao, L., Chen, N., & Li, J. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107776 · Cited by 1 · see citing works
Borkar, K., Reen, A. S., Jawahar, C. V., & Arora, C. (2024) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3702250.3702275 · Cited by 1 · see citing works
Marinov, Z., Jaus, A., Kleesiek, J., & Stiefelhagen, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81854-7_7 · Cited by 1 · see citing works
- Authors
- Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Sensetime (China) (CN)
- Shanghai Jiao Tong University (CN)
- Universitair Ziekenhuis Leuven (BE)
- King's College London (GB)
- Corresponding author
- Wang, Guotai
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 72
- Pages
- 102102
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7613452
- OpenAlex
- W3159178846
- Cited by
- 126
- References
- 61
- Citations by year
- 2026: 10 · 2025: 36 · 2024: 29 · 2023: 31 · 2022: 18 · 2021: 2
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- EPSRC
- Wellcome Trust
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- interactive segmentation, medical image segmentation, unseen objects, generalization, data fusion, organ segmentation, lesion segmentation, 3D medical images
- MeSH terms
- Deep Learning, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
