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Automatic segmentation methods are an important advancement in medical image analysis.
The KiTS19 challenge data: 300 kidney tumor cases with clinical context
Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M. et al. (2019) · 1904
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Müller, D., & Kramer, F. (2019) · 1910
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A review on image segmentation techniques
Pal, N. R., & Pal, S. K. (1993) · 1993
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Ridge-based vessel segmentation in color images of the retina
Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & Van Ginneken, B. (2004) · 2004
Earlier work this paper cites.
Generalized overlap measures for evaluation and validation in medical image analysis
Crum, W. R., Camara, O., & Hill, D. L. G. (2006) · 2006
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Renal tumor quantification and classification in contrast-enhanced abdominal CT
Linguraru, M. G., Yao, J., Gautam, R., Peterson, J., Li, Z., Linehan, W. M., & Summers, R. M. (2009) · 2008
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., & Bengio, Y. (2010) · 2010
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BEaST: brain extraction based on nonlocal segmentation technique
Eskildsen, S. F., Coupé, P., Fonov, V., Manjón, J. V., Leung, K. K., Guizard, N., Wassef, S. N., Østergaard, L. R., Collins, D. L., Initiative, A. D. N. et al. (2012) · 2011
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Henry, T., Carre, A., Lerousseau, M., Estienne, T., Robert, C., Paragios, N., & Deutsch, E. (2020) · 2011
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Machine learning and radiology
Wang, S., & Summers, R. M. (2012) · 2012
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The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R. et al. (2014) · 2014
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Atlas-based under-segmentation
Wachinger, C., & Golland, P. (2014) · 2014
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Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · 2015
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · 2015
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SegNet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · 2016
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3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · 2016
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V-Net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016
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Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments
Rundo, L., Militello, C., Vitabile, S., Casarino, C., Russo, G., Midiri, M., & Gilardi, M. C. (2016) · 2016
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Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · 2017
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Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks
Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., & Vercauteren, T. (2017) · 2017
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Brain tumor segmentation with deep neural networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · 2017
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · 2017
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Deep learning applications in medical image analysis
Ker, J., Wang, L., Rao, J., & Lim, T. (2018) · 2017
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Weighted level set evolution based on local edge features for medical image segmentation
Khadidos, A., Sanchez, V., & Li, C.-T. (2017) · 2017
Cited alongside, same era.
Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies
Kim, N. H., Jung, Y. S., Jeong, W. S., Yang, H.-J., Park, S.-K., Choi, K., & Park, D. I. (2017) · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017
Cited alongside, same era.
A fully automatic approach for multimodal PET and MR image segmentation in Gamma Knife treatment planning
Rundo, L., Stefano, A., Militello, C., Russo, G., Sabini, M. G., D’Arrigo, C., Marletta, F., Ippolito, M., Mauri, G., Vitabile, S., & Gilardi, M. C. (2017) · 2017
Cited alongside, same era.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017) · 2017
Cited alongside, same era.
A novel framework for MR image segmentation and quantification by using MedGA
Rundo, L., Tangherloni, A., Cazzaniga, P., Nobile, M. S., Russo, G., Gilardi, M. C. et al. (2019b) · 2019
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Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · 2019
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Combo loss: Handling input and output imbalance in multi-organ segmentation
Taghanaki, S. A., Zheng, Y., Zhou, S. K., Georgescu, B., Sharma, P., Xu, D., Comaniciu, D., & Hamarneh, G. (2019) · 2019
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Normalization in training U-Net for 2-D biomedical semantic segmentation
Zhou, X.-Y., & Yang, G.-Z. (2019) · 2019
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Boundary-weighted domain adaptive neural network for prostate mr image segmentation
Zhu, Q., Du, B., & Yan, P. (2019a) · 2019
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Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Cardoso, M. J. (2017) · 2017
Cited alongside, same era.
Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M. H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A. K., & Marti, R. (2017) · 2017
Cited alongside, same era.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M. et al. (2018) · 2018
Cited alongside, same era.
A survey of graph cuts/graph search based medical image segmentation
Chen, X., & Pan, L. (2018) · 2018
Cited alongside, same era.
Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection
Hashemi, S. R., Salehi, S. S. M., Erdogmus, D., Prabhu, S. P., Warfield, S. K., & Gholipour, A. (2018) · 2018
Cited alongside, same era.
nnU-net: Self-adapting framework for U-net-based medical image segmentation
Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P. F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S. et al. (2018) · 2018
Cited alongside, same era.
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor mri using an unsupervised machine learning technique
Rundo, L., Militello, C., Tangherloni, A., Russo, G., Vitabile, S., Gilardi, M. C., & Mauri, G. (2018) · 2018
Cited alongside, same era.
An RDAU-NET model for lesion segmentation in breast ultrasound images
Zhuang, Z., Li, N., Joseph Raj, A. N., Mahesh, V. G., & Qiu, S. (2019) · 2019
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Ensemble U-Net-based method for fully automated detection and segmentation of renal masses on computed tomography images
Fatemeh, Z., Nicola, S., Satheesh, K., & Eranga, U. (2020) · 2020
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The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge
Heller, N., Isensee, F., Maier-Hein, K. H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M. et al. (2021) · 2020
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A survey of loss functions for semantic segmentation
Jadon, S. (2020) · 2020
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The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs
Kessler, D. A., MacKay, J. W., Crowe, V. A., Henson, F. M., Graves, M. J., Gilbert, F. J., & Kaggie, J. D. (2020) · 2020
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Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT
Kim, T., Lee, K., Ham, S., Park, B., Lee, S., Hong, D., Kim, G. B., Kyung, Y. S., Kim, C.-S., & Kim, N. (2020) · 2020
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BraTS toolkit: Translating BraTS brain tumor segmentation algorithms into clinical and scientific practice
Kofler, F., Berger, C., Waldmannstetter, D., Lipkova, J., Ezhov, I., Tetteh, G., Kirschke, J., Zimmer, C., Wiestler, B., & Menze, B. H. (2020) · 2020
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A survey on U-shaped networks in medical image segmentations
Liu, L., Cheng, J., Quan, Q., Wu, F.-X., Wang, Y.-P., & Wang, J. (2020) · 2020
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Exploring uncertainty measures in bayesian deep attentive neural networks for prostate zonal segmentation
Liu, Y., Yang, G., Hosseiny, M., Azadikhah, A., Mirak, S. A., Miao, Q., Raman, S. S., & Sung, K. (2020) · 2020
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Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering
Rundo, L., Beer, L., Ursprung, S., Martin-Gonzalez, P., Markowetz, F., Brenton, J. D., Crispin-Ortuzar, M., Sala, E., & Woitek, R. (2020a) · 2020
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A survey on nature-inspired medical image analysis: a step further in biomedical data integration
Rundo, L., Militello, C., Vitabile, S., Russo, G., Sala, E., & Gilardi, M. C. (2020b) · 2020
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Eigenloss: Combined PCA-Based loss function for polyp segmentation
Sánchez-Peralta, L. F., Picón, A., Antequera-Barroso, J. A., Ortega-Morán, J. F., Sánchez-Margallo, F. M., & Pagador, J. B. (2020) · 2020
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Image segmentation evaluation: a survey of methods
Wang, Z., Wang, E., & Zhu, Y. (2020) · 2020
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AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M. et al. (2021) · 2021
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nnU-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · 2021
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Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · 2021
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Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
Nazir, M., Shakil, S., & Khurshid, K. (2021) · 2021
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Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2021) · 2021
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