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Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.
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IEEE Transactions on Image Processing 10(2), 266–277 (2001)
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IEEE Transactions on Image Processing 17(11), 2029–2039 (2008)
Lankton, S., Tannenbaum, A.: Localizing region-based active contours · 2008
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In: LNCS vol. 9351 (Proc. MICCAI). pp. 234–241 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation · 2015
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IEEE Transactions on Medical Imaging 36(3), 781–791 (2017)
Hoogi, A., Subramaniam, A., Veerapaneni, R., Rubin, D.L.: Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis · 2017
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In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2017)
Hu, P., Shuai, B., Liu, J., Wang, G.: Deep level sets for salient object detection · 2017
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In: Deep Learning in Medical Image Analysis, Lecture Notes in Computer Science, vol. 11045, pp. 282–290. Springer (2018)
Imran, A.A.Z., Hatamizadeh, A., Ananth, S.P., Ding, X., Terzopoulos, D., Tajbakhsh, N.: Automatic segmentation of pulmonary lobes using a progressive dense V-network · 2018
Later among the works it cites.
In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 8877–8885 (2018)
Marcos, D., Tuia, D., Kellenberger, B., Zhang, L., Bai, M., Liao, R., Urtasun, R.: Learning deep structured active contours end-to-end · 2018
Later among the works it cites.
arXiv preprint arXiv:1905.12120 (2019)
Hatamizadeh, A., Hosseini, H., Liu, Z., Schwartz, S.D., Terzopoulos, D.: Deep dilated convolutional nets for the automatic segmentation of retinal vessels · 2019
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