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The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming.
Interactive segmentation of medical images through fully convolutional neural networks
Sakinis, T., Milletari, F., Roth, H., Korfiatis, P., Kostandy, P.M., Philbrick, K., Akkus, Z., Xu, Z., Xu, D., Erickson, B.J., 2019 · 1903
Earlier work this paper cites.
Riba, E., Mishkin, D., Ponsa, D., Rublee, E., Bradski, G., 2020 · 1910
Earlier work this paper cites.
Snakes: Active Contour Models
Kass, M., Witkin, A., Terzopoulos, D., 1988 · 1988
Earlier work this paper cites.
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
Osher, S., Sethian, J.A., 1988 · 1988
Earlier work this paper cites.
Seeded Region Growing
Adams, R., Bischof, L., 1994 · 1994
Earlier work this paper cites.
An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision
Boykov, Y., Kolmogorov, V., 2004 · 2004
Earlier work this paper cites.
" grabcut" interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., Blake, A., 2004 · 2004
Earlier work this paper cites.
Graph cuts and efficient N-D image segmentation
Boykov, Y., Funka-Lea, G., 2006 · 2006
Earlier work this paper cites.
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Yushkevich, P.A., Piven, J., Cody Hazlett, H., Gimpel Smith, R., Ho, S., Gee, J.C., Gerig, G., 2006 · 2006
Earlier work this paper cites.
The extensible neuroimaging archive toolkit: An informatics platform for managing, exploring, and sharing neuroimaging data
Marcus, D.S., Olsen, T.R., Ramaratnam, M., Buckner, R.L., 2007 · 2007
Earlier work this paper cites.
Geos: Geodesic image segmentation, in: European Conference on Computer Vision, Springer. pp. 99–112
Criminisi, A., Sharp, T., Blake, A., 2008 · 2008
Earlier work this paper cites.
Active learning for interactive 3d image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 603–610
Top, A., Hamarneh, G., Abugharbieh, R., 2011 · 2011
Earlier work this paper cites.
3D Slicer as an Image Computing Platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J., Aylward, S., Miller, J.V., Pieper, S., Kikinis, R., 2012 · 2012
Earlier work this paper cites.
Active learning
Settles, B., 2012 · 2012
Earlier work this paper cites.
The medical imaging interaction toolkit: Challenges and advances: 10 years of open-source development
Nolden, M., Zelzer, S., Seitel, A., Wald, D., Müller, M., Franz, A.M., Maleike, D., Fangerau, M., Baumhauer, M., Maier-Hein, L., Maier-Hein, K.H., Meinzer, H.P., Wolf, I., 2013 · 2013
Earlier work this paper cites.
An Overview of Interactive Medical Image Segmentation
Zhao, F., Xie, X., 2013 · 2013
Earlier work this paper cites.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation, in: Medical Image Computing and Computer-Assisted Intervention, pp. 424–432
Çiçek, O., Abdulkadir, A., Lienkamp, S., Brox, T., Ronneberger, O., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Enhancing a diffusion algorithm for 4D image segmentation using local information, in: SPIE Medical Imaging 2016: Image Processing
Lösel, P., Heuveline, V., 2016 · 2016
Cited alongside, same era.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, in: 3DV
Milletari, F., Navab, N., Ahmadi, S.a., 2016 · 2016
Cited alongside, same era.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2020 · 2020
Later among the works it cites.
Introducing Biomedisa as an open-source online platform for biomedical image segmentation
Lösel, P.D., van de Kamp, T., Jayme, A., Ershov, A., Faragó, T., Pichler, O., Tan Jerome, N., Aadepu, N., Bremer, S., Chilingaryan, S.A., Heethoff, M., Kopmann, A., Odar, J., Schmelzle, S., Zuber, M., Wittbrodt, J., Baumbach, T., Heuveline, V., 2020 · 2020
Later among the works it cites.
MONAI: Medical Open Network for AI
MONAI Consortium, 2020 · 2020
Later among the works it cites.
Diminishing uncertainty within the training pool: Active learning for medical image segmentation
Nath, V., Yang, D., Landman, B.A., Xu, D., Roth, H.R., 2020 · 2020
Later among the works it cites.
The medical segmentation decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., AnnetteKopp-Schneider, Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P.F., Do, R.K.G., Gollub, M.J., Heckers, S.H., Huisman, H., Jarnagin, W.R., McHugo, M.K., Napel, S., Pernicka, J.S.G., Rhode, K., Tobon-Gomez, C., Vorontsov, E., Huisman, H., Meakin, J.A., Ourselin, S., Wiesenfarth, M., Arbelaez, P., Bae, B., Chen, S., Daza, L., Feng, J., He, B., Isensee, F., Ji, Y., Jia, F., Kim, N., Kim, I., Merhof, D., Pai, A., Park, B., Perslev, M., Rezaiifar, R., Rippel, O., Sarasua, I., Shen, W., Son, J., Wachinger, C., Wang, L., Wang, Y., Xia, Y., Xu, D., Xu, Z., Zheng, Y., Simpson, A.L., Maier-Hein, L., Cardoso, M.J., 2021 · 2021
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Xu, N., Price, B., Cohen, S., Yang, J., Huang, T., 2016 · 2016
Cited alongside, same era.
Deep bayesian active learning with image data, in: International Conference on Machine Learning, PMLR. pp. 1183–1192
Gal, Y., Islam, R., Ghahramani, Z., 2017 · 2017
Cited alongside, same era.
Lesiontracker: extensible open-source zero-footprint web viewer for cancer imaging research and clinical trials
Urban, T., Ziegler, E., Lewis, R., Hafey, C., Sadow, C., Van den Abbeele, A.D., Harris, G.J., 2017 · 2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
Cited alongside, same era.
Suggestive annotation: A deep active learning framework for biomedical image segmentation, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 399–407
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z., 2017 · 2017
Cited alongside, same era.
Cost-sensitive active learning for intracranial hemorrhage detection, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 715–723
Kuo, W., Häne, C., Yuh, E., Mukherjee, P., Malik, J., 2018 · 2018
Cited alongside, same era.
Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L., 2018 · 2018
Cited alongside, same era.
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., Vercauteren, T., 2018 · 2018
Cited alongside, same era.
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Nci imaging data commons
Fedorov, A., Longabaugh, W.J., Pot, D., Clunie, D.A., Pieper, S., Aerts, H.J., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M.D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D.P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K., Kikinis, R., 2021 · 2021
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UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Yang, D., Roth, H., Xu, D., 2021 · 2021
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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
He, Y., Yang, D., Roth, H., Zhao, C., Xu, D., 2021 · 2021
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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 · 2021
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NVIDIA AI-Assisted Annotation (AIAA) - Clara Train SDK
NVIDIA, 2021 · 2021
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Ronneberger, O., Fischer, P., Brox, T., 2015 · 2021
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Going to extremes: Weakly supervised medical image segmentation
Roth, H.R., Yang, D., Xu, Z., Wang, X., Xu, D., 2021 · 2021
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DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images, in: Data Augmentation, Labelling, and Imperfections: Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, Springer. pp. 11–21
Diaz-Pinto, A., Mehta, P., Alle, S., Asad, M., Brown, R., Nath, V., Ihsani, A., Antonelli, M., Palkovics, D., Pinter, C., et al., 2022 · 2022
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CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.N., Xiao, J., Lu, Y., Landman, B.A., Yuan, Y., Yuille, A., Tang, Y., Zhou, Z., 2023 · 2023
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A review of uncertainty estimation and its application in medical imaging
Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H., 2023 · 2023
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