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Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities.
Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge. vol. 5, p. 12 (2015)
2015
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Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE transactions on medical imaging 37
2018
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Löffler, M.T., Sekuboyina, A., Jacob, A., Grau, A.L., Scharr, A., El Husseini, M., Kallweit, M., Zimmer, C., Baum, T., Kirschke, J.S.: A vertebral segmentation dataset with fracture grading. Radiology: Artificial Intelligence 2
2020
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2021
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Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18
2021
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Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., Zhang, S.: Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning. Medical image analysis 72
2021
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Payette, K., de Dumast, P., Kebiri, H., Ezhov, I., Paetzold, J.C., Shit, S., Iqbal, A., Khan, R., Kottke, R., Grehten, P., et al.: An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Scientific data 8
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021)
2021
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Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation (2021)
2021
Earlier work this paper cites.
Wang, X., Jiang, L., Li, L., Xu, M., Deng, X., Dai, L., Xu, X., Li, T., Guo, Y., Wang, Z., Dragotti, P.L.: Joint learning of 3d lesion segmentation and classification for explainable covid-19 diagnosis. IEEE Transactions on Medical Imaging 40
2021
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Zhang, J., Xie, Y., Xia, Y., Shen, C.: Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1195–1204 (2021)
2021
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Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 574–584 (2022)
2022
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2022
Earlier work this paper cites.
Radl, L., Jin, Y., Pepe, A., Li, J., Gsaxner, C., Zhao, F.h., Egger, J.: Avt: Multicenter aortic vessel tree cta dataset collection with ground truth segmentation masks. Data in brief 40
2022
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Wang, H., Yi, H., Liu, J., Gu, L.: Integrated treatment planning in percutaneous microwave ablation of lung tumors. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). pp. 4974–4977. IEEE (2022)
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Chen, H., Zhao, X., Sun, H., Dou, J., Du, C., Yang, R., Lin, X., Yu, S., Liu, J., Yuan, C., Balu, N.: Cerebral artery segmentation challenge. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2023)
2023
2023
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2023
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2023
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Ma, J., Wang, B.: Segment anything in medical images. arXiv preprint arXiv:2304.12306 (2023)
2023
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Cited alongside, same era.
Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., Li, K.: Sam on medical images: A comprehensive study on three prompt modes (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L.W., Bao, S., Landman, B.A., Wheless, L.E., Coburn, L.A., Wilson, K.T., Wang, Y., Zhao, S., Fogo, A.B., Yang, H., Tang, Y., Huo, Y.: Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Gatidis, S., Früh, M., Fabritius, M., Gu, S., Nikolaou, K., La Fougère, C., Ye, J., He, J., Peng, Y., Bi, L., et al.: The autopet challenge: Towards fully automated lesion segmentation in oncologic pet/ct imaging (2023)
2023
Cited alongside, same era.
2023
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Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Zhao, Z., Chen, H., Wang, L., Golts, A., Khapun, D., Shats, D., Shoshan, Y., Gilboa-Solomon, F., George, Y., Yang, X., Zhang, J., Zhang, J., Xia, Y., Wu, M., Liu, Z., Walczak, E., McSweeney, S., Vasdev, R., Hornung, C., Solaiman, R., Schoephoerster, J., Abernathy, B., Wu, D., Abdulkadir, S., Byun, B., Spriggs, J., Struyk, G., Austin, A., Simpson, B., Hagstrom, M., Virnig, S., French, J., Venkatesh, N., Chan, S., Moore, K., Jacobsen, A., Austin, S., Austin, M., Regmi, S., Papanikolopoulos, N., Weight, C.: The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct (2023)
2023
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Hu, C., Xia, T., Ju, S., Li, X.: When sam meets medical images: An investigation of segment anything model (sam) on multi-phase liver tumor segmentation (2023)
2023
Cited alongside, same era.
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Medical Image Analysis 89
2023
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Wang, K.: Tumor detection, segmentation and classification challenge on automated 3d breast ultrasound (abus) 2023 (2023), https://tdsc-abus2023.grand-challenge.org/TDSC-ABUS2023/
2023
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Wasserthal, J., Breit, H.C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., Bach, M., Segeroth, M.: Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence 5
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Zhou, T., Zhang, Y., Zhou, Y., Wu, Y., Gong, C.: Can sam segment polyps? (2023)
2023
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Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Wang, J., Wang, L., Gao, J., Lee, Y.J.: Segment everything everywhere all at once (2023)
2023
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