K. W. Dunn, R. M. Sandoval, K. J. Kelly, P. C. Dagher, G. A. Tanner, S. J. Atkinson, R. L. Bacallao, and B. A. Molitoris, “Functional studies of the kidney of living animals using multicolor two-photon microscopy,” American Journal of Physiology-Cell Physiology, vol. 283, no. 3, pp. C905-C916, September 2002.
 M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, January 1988.
 R. Delgado-Gonzalo, V. Uhlmann, D. Schmitter, and M. Unser, “Snakes on a plane: A perfect snap for bioimage analysis,” IEEE Signal Processing Magazine, vol. 32, no. 1, pp. 41-48, January 2015.
 B. Li and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2096-2106, August 2007.
 T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266-277, February 2001.
 K.S. Lorenz, P. Salama, K.W. Dunn, and E.J. Delp, “Three dimensional segmentation of fluorescence microscopy images using active surfaces,” Proceedings of the IEEE International Conference on Image Processing, pp. 1153-1157, September 2013, Melbourne, Australia.
 S. Lee, P. Salama, K. W. Dunn, and E. J. Delp, “Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction,” Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 709-713, April 2017, Melbourne, Australia.
 S. Lee, P. Salama, K. W. Dunn, and E. J. Delp, “Boundary fitting based segmentation of fluorescence microscopy images,” Proceedings of the IS&T/SPIE Conference on Imaging and Multimedia Analytics in a Web and Mobile World 2015, pp. 940805-1-10, February 2015, San Francisco, CA.
 G. Paul, J. Cardinale, and I. F. Sbalzarini, “Coupling image restoration and segmentation: A generalized linear model/Bregman perspective,” International Journal of Computer Vision, vol. 104, no. 1, pp. 69-93, March 2013.
 A. Rizk, G. Paul, P. Incardona, M. Bugarski, M. Mansouri, A. Niemann, U. Ziegler, P. Berger, and I. F. Sbalzarini, “Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh,” Nature Protocols, vol. 9, no. 3, pp. 586-596, February 2014.
 G. Srinivasa, M. C. Fickus, Y. Guo, A. D. Linstedt, and J. Kovacevic, “Active mask segmentation of fluorescence microscope images,” IEEE Transactions on Image Processing, vol. 18, no. 8, pp. 1817-1829, August 2009.
 J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, June 2015, Boston, MA.
 G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A.W.M. van der Laak, B. van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,” arXiv preprint arXiv:1702.05747, February 2017. [OpenAIRE]
 O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” Proceedings of the Medical Image Computing and Computer-Assisted Intervention, pp. 231-241, October 2015, Munich, Germany.
 S. E. A. Raza, L. Cheung, D. Epstein, S. Pelengaris, M. Khan, and N. Rajpoot, “MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images,” Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 337-340, April 2017, Melbourne, Australia.