ABSTRACT
Semantic segmentation of colon gland is notoriously challenging due to their complex texture, huge variation, and the scarcity of training data with accurate annotations. It is even hard for experts, let alone computer-aided diagnosis systems. Recently, some deep convolutional neural networks (DCNN) based methods have been introduced to tackle this problem, achieving much impressive performance. However, these methods always tend to miss segmented results for the important regions of colon gland or make a wrong segmenting decision.In this paper, we address the challenging problem by proposed a novel framework through conditional generative adversarial network. First, the generator in the framework is trained to learn a mapping from gland colon image to a confidence map indicating the probabilities of being a pixel of gland object. The discriminator is responsible to penalize the mismatch between colon gland image and the confidence map. This additional adversarial learning facilitates the generator to produce higher quality confidence map. Then we transform the confidence map into a binary image using a fixed threshold to fulfill the segmentation task. We implement extensive experiments on the public benchmark MICCAI gland 2015 dataset to verify the effectiveness of the proposed method. Results demonstrate that our method achieve a better segmentation result in terms of visual perception and two quantitative metrics, compared with other methods.
- Altunbay, D., Cigir, C., Sokmensuer, C., & Gunduz-Demir, C. (2010). Color graphs for automated cancer diagnosis and grading. IEEE Transactions on Biomedical Engineering, 57(3), 665.Google ScholarCross Ref
- Gunduz-Demir, C., Kandemir, M., Tosun, A. B., & Sokmensuer, C. (2010). Automatic segmentation of colon glands using object-graphs. Medical Image Analysis, 14(1), 1--12.Google ScholarCross Ref
- Fu, H., Qiu, G., Shu, J., & Ilyas, M. (2014). A novel polar space random field model for the detection of glandular structures. IEEE Transactions on Medical Imaging, 33(3), 764.Google ScholarCross Ref
- Sirinukunwattana, K., Snead, D. R. J., & Rajpoot, N. M. (2015). A stochastic polygons model for glandular structures in colon histology images. IEEE Transactions on Medical Imaging, 34(11), 2366--2378.Google ScholarCross Ref
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097--1105). Google ScholarDigital Library
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation, 9351, 234--241.Google Scholar
- Huang, G., Liu, Z., Laurens, V. D. M., & Weinberger, K. Q. (2016). Densely connected convolutional networks. 2261--2269.Google Scholar
- Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., & Ozair, S., et al. (2014). Generative adversarial nets. International Conference on Neural Information Processing Systems (Vol.3, pp.2672--2680). MIT Press. Google ScholarDigital Library
- Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. Computer Science, 2672--2680. Google ScholarDigital Library
- Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2016). Image-to-image translation with conditional adversarial networks. 5967--5976.Google Scholar
- Zhang, H., Sindagi, V., & Patel, V. M. (2017). Image de-raining using a conditional generative adversarial network.Google Scholar
- Yi, X., & Babyn, P. (2017). Sharpness-aware low-dose ct denoising using conditional generative adversarial network. Journal of Digital Imaging, 1--15.Google Scholar
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770--778).Google ScholarCross Ref
- Sirinukunwattana, K., Pluim, J. P., Chen, H., Qi, X., Heng, P. A., Guo, Y. B., ... & Böhm, A. (2017). Gland segmentation in colon histology images: The glas challenge contest. Medical image analysis, 35, 489--502.Google Scholar
- Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., ... & Lerer, A. (2017). Automatic differentiation in pytorch.Google Scholar
- Kingma, D., & Ba, J. (2014). Adam: a method for stochastic optimization. Computer Science.Google Scholar
- Real, R., & Vargas, J. M. (1996). The probabilistic basis of jaccard's index of similarity. Systematic Biology, 45(3), 380--385.Google ScholarCross Ref
Index Terms
- Semantic Segmentation of Colon Gland with Conditional Generative Adversarial Network
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