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Accurate segmentation of the heart is an important step towards evaluating cardiac function.
Krähenbühl, P., Koltun, V.: Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. In: NIPS. pp. 109–117 (2011)
2011
Earlier work this paper cites.
Bai, W., Shi, W., O’Regan, D.P., Tong, T., Wang, H., Jamil-Copley, S., Peters, N.S., Rueckert, D.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Transactions on Medical Imaging 32(7), 1302–15 (2013)
2013
Earlier work this paper cites.
Nichols, M., Townsend, N., Scarborough, P., Rayner, M.: Cardiovascular disease in Europe 2014 : epidemiological update. European heart journal (2014)
2014
Earlier work this paper cites.
Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med Image Anal 19(1), 98–109 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In: ICCV. pp. 1026–34 (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: ICML. pp. 448–456 (2015)
2015
Cited alongside, same era.
Kingma, D.P., Ba, J.L.: ADAM: A Method for Stochastic Optimization. In: ICLR (2015)
2015
Cited alongside, same era.
Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. In: CVPR. pp. 343 –3440 (2015)
2015
Cited alongside, same era.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: MICCAI. pp. 234–241 (2015)
2015
Cited alongside, same era.
Avendi, R.M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 30, 108–119 (2016)
2016
Cited alongside, same era.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: MICCAI. pp. 424–432 (2016)
2016
Later among the works it cites.
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: 3D Vision. pp. 565 – 571 (2016)
2016
Later among the works it cites.
2017
Closest in time.
Oktay, O., Bai, W., Guerrero, R., Rajchl, M., de Marvao, A., O’Regan, D.P., Cook, S.A., Heinrich, M.P., Glocker, B., Rueckert, D.: Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images. IEEE Trans Med Imag 36(1), 332–342 (2017)
2017
Closest in time.
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2017
Closest in time.