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Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy.
The liver tumor segmentation benchmark (lits)
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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., 2019 · 1902
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Han-seg: The head and neck organ-at-risk ct and mr segmentation dataset
Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, B., Vrtovec, T., 2023 · 1927
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Comparison of treatment plans involving intensity-modulated radiotherapy for nasopharyngeal carcinoma
Xia, P., Fu, K.K., Wong, G.W., Akazawa, C., Verhey, L.J., 2000 · 2000
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Intensity-modulated radiotherapy in nasopharyngeal carcinoma: dosimetric advantage over conventional plans and feasibility of dose escalation
Kam, M.K., Chau, R.M., Suen, J., Choi, P.H., Teo, P.M., 2003 · 2003
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Defining the tumour and target volumes for radiotherapy
Burnet, N.G., Thomas, S.J., Burton, K.E., Jefferies, S.J., 2004 · 2004
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User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., 2006 · 2006
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Tumor delineation: The weakest link in the search for accuracy in radiotherapy
Njeh, C., 2008 · 2008
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The association between the development of radiation therapy, image technology, and chemotherapy, and the survival of patients with nasopharyngeal carcinoma: a cohort study from 1990 to 2012
Sun, X.S., Liu, S.L., Luo, M.J., Li, X.Y., Chen, Q.Y., Guo, S.S., Wen, Y.F., Liu, L.T., Xie, H.J., Tang, Q.N., et al., 2019 · 2012
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Randomized phase iii trial of concurrent accelerated radiation plus cisplatin with or without cetuximab for stage iii to iv head and neck carcinoma: Rtog 0522
Ang, K.K., Zhang, Q., Rosenthal, D.I., Nguyen-Tan, P.F., Sherman, E.J., Weber, R.S., Galvin, J.M., Bonner, J.A., Harris, J., El-Naggar, A.K., et al., 2014 · 2014
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Management of nasopharyngeal carcinoma: current practice and future perspective
Lee, A., Ma, B., Ng, W.T., Chan, A., et al., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Nasopharyngeal carcinoma
Chua, M.L., Wee, J.T., Hui, E.P., Chan, A.T., 2016 · 2016
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Focal loss for dense object detection, in: ICCV, pp. 2980–2988
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P., 2017 · 2017
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The effect of contrast material on radiation dose at ct: Part ii. a systematic evaluation across 58 patient models
Sahbaee, P., Abadi, E., Segars, W.P., Marin, D., Nelson, R.C., Samei, E., 2017 · 2017
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Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Vallieres, M., Kay-Rivest, E., Perrin, L.J., Liem, X., Furstoss, C., Aerts, H.J., Khaouam, N., Nguyen-Tan, P.F., Wang, C.S., Sultanem, K., et al., 2017 · 2017
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Identifying the best machine learning algorithms for brain tumor segmentation
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R., Berger, C., Ha, S., Rozycki, M., et al., 2018 · 2018
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Automatic multi-organ segmentation on abdominal ct with dense v-networks
Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J., Barratt, D.C., 2018 · 2018
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International guideline for the delineation of the clinical target volumes (ctv) for nasopharyngeal carcinoma
Lee, A.W., Ng, W.T., Pan, J.J., Poh, S.S., Ahn, Y.C., AlHussain, H., Corry, J., Grau, C., Grégoire, V., Harrington, K.J., et al., 2018 · 2018
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Automatic multiorgan segmentation in thorax ct images using u-net-gan
Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W.J., Liu, T., Yang, X., 2019 · 2019
Cited alongside, same era.
Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3d images
Feng, X., Qing, K., Tustison, N.J., Meyer, C.H., Chen, Q., 2019 · 2019
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Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer
Kosmin, M., Ledsam, J., Romera-Paredes, B., Mendes, R., Moinuddin, S., de Souza, D., Gunn, L., Kelly, C., Hughes, C., Karthikesalingam, A., et al., 2019 · 2019
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The tumor target segmentation of nasopharyngeal cancer in ct images based on deep learning methods
Li, S., Xiao, J., He, L., Peng, X., Yuan, X., 2019 · 2019
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Deep learning for automated contouring of primary tumor volumes by mri for nasopharyngeal carcinoma
Lin, L., Dou, Q., Jin, Y.M., Zhou, G.Q., Tang, Y.Q., Chen, W.L., Su, B.A., Liu, F., Tao, C.J., Jiang, N., et al., 2019 · 2019
Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study
Nikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., Patel, Y., Meyer, C., Askham, H., Romera-Paredes, B., et al., 2021 · 2021
Later among the works it cites.
Guidelines for radiotherapy of nasopharyngeal carcinoma
Wang, R., Kang, M., 2021 · 2021
Later among the works it cites.
Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era
Jin, D., Guo, D., Ge, J., Ye, X., Lu, L., 2022 · 2022
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Npcnet: jointly segment primary nasopharyngeal carcinoma tumors and metastatic lymph nodes in mr images
Li, Y., Dan, T., Li, H., Chen, J., Peng, H., Liu, L., Cai, H., 2022 · 2022
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Automatic delineation of gross tumor volume based on magnetic resonance imaging by performing a novel semisupervised learning framework in nasopharyngeal carcinoma
Liao, W., He, J., Luo, X., Wu, M., Shen, Y., Li, C., Xiao, J., Wang, G., Chen, N., 2022 · 2022
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Cited alongside, same era.
Clinically applicable deep learning framework for organs at risk delineation in ct images
Tang, H., Chen, X., Liu, Y., Lu, Z., You, J., Yang, M., Yao, S., Zhao, G., Xu, Y., Chen, T., et al., 2019 · 2019
Cited alongside, same era.
Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy
Zhu, W., Huang, Y., Zeng, L., Chen, X., Liu, Y., Qian, Z., Du, N., Fan, W., Xie, X., 2019 · 2019
Cited alongside, same era.
Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4223–4232
Guo, D., Jin, D., Zhu, Z., Ho, T.Y., Harrison, A.P., Chao, C.H., Xiao, J., Lu, L., 2020 · 2020
Cited alongside, same era.
Bias: Transparent reporting of biomedical image analysis challenges
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A.L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., et al., 2020 · 2020
Cited alongside, same era.
Automated delineation of nasopharynx gross tumor volume for nasopharyngeal carcinoma by plain ct combining contrast-enhanced ct using deep learning
Wang, X., Yang, G., Zhang, Y., Zhu, L., Xue, X., Zhang, B., Cai, C., Jin, H., Zheng, J., Wu, J., et al., 2020 · 2020
Cited alongside, same era.
A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
Chen, X., Sun, S., Bai, N., Han, K., Liu, Q., Yao, S., Tang, H., Zhang, C., Lu, Z., Huang, Q., et al., 2021 · 2021
Cited alongside, same era.
Crossmoda 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation
Dorent, R., Kujawa, A., Ivory, M., Bakas, S., Rieke, N., Joutard, S., Glocker, B., Cardoso, J., Modat, M., Batmanghelich, K., et al., 2023 · 2021
Cited alongside, same era.
Later among the works it cites.
Head and neck tumor segmentation in pet/ct: the hecktor challenge
Oreiller, V., Andrearczyk, V., Jreige, M., Boughdad, S., Elhalawani, H., Castelli, J., Vallieres, M., Zhu, S., Xie, J., Peng, Y., et al., 2022 · 2022
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Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
Shi, F., Hu, W., Wu, J., Han, M., Wang, J., Zhang, W., Zhou, Q., Zhou, J., Wei, Y., Shao, Y., et al., 2022 · 2022
Later among the works it cites.
Efficient detection model of steel strip surface defects based on yolo-v7
Wang, Y., Wang, H., Xin, Z., 2022 · 2022
Later among the works it cites.
Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study
Ye, X., Guo, D., Ge, J., Yan, S., Xin, Y., Song, Y., Yan, Y., Huang, B.s., Hung, T.M., Zhu, Z., et al., 2022 · 2022
Later among the works it cites.
Huang, Z., Wang, H., Deng, Z., Ye, J., Su, Y., Sun, H., He, J., Gu, Y., Gu, L., Zhang, S., et al., 2023 · 2023
Closest in time.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023 · 2023
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Clip-driven universal model for organ segmentation and tumor detection, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21152–21164
Liu, J., Zhang, Y., Chen, J.N., Xiao, J., Lu, Y., A Landman, B., Yuan, Y., Yuille, A., Tang, Y., Zhou, Z., 2023 · 2023
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Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study
Luo, X., Liao, W., He, Y., Tang, F., Wu, M., Shen, Y., Huang, H., Song, T., Li, K., Zhang, S., et al., 2023 · 2023
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MultiTalent: A multi-dataset approach to medical image segmentation, in: MICCAI, pp. 648–658
Ulrich, C., Isensee, F., Wald, T., Zenk, M., Baumgartner, M., Maier-Hein, K.H., 2023 · 2023
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TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images
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., 2023 · 2023
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Anatomy-aware lymph node detection in chest ct using implicit station stratification
Yan, K., Jin, D., Guo, D., Xu, M., Shen, N., Hua, X.S., Ye, X., Lu, L., 2023 · 2023
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UniSeg: A prompt-driven universal segmentation model as well as a strong representation learner, in: MICCAI, p. 508—518
Ye, Y., Xie, Y., Zhang, J., Chen, Z., Xia, Y., Xia, Y., 2023 · 2023
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A statistical deformation model-based data augmentation method for volumetric medical image segmentation
He, W., Zhang, C., Dai, J., Liu, L., Wang, T., Liu, X., Jiang, Y., Li, N., Xiong, J., Wang, L., et al., 2024 · 2024
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Evaluation of segmentation methods on head and neck ct: auto-segmentation challenge 2015
Raudaschl, P.F., Zaffino, P., Sharp, G.C., Spadea, M.F., Chen, A., Dawant, B.M., Albrecht, T., Gass, T., Langguth, C., Lüthi, M., et al., 2017 · 2036
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