Abstract
Convolutional neural networks (CNNs) have obtained enormous success in image segmentation, which is substantial in many clinical treatments. Even though CNNs have achieved state-of-the-art performances, most researches on semantic segmentation using the deep learning methods are in the field of computer vision, so the research on medical images is much less mature than that of natural images, especially, in the field of 3D image segmentation. Our experiments on CNN segmentation models demonstrated that, with modifications and tuning in network architecture and parameters, modified models would show better performances in the selected task, especially with limited training dataset and hardware. We have selected the 3D liver segmentation as our goal and presented a pathway to select a state-of-the-art CNN model and improve it for our specific task and data. Our modifications include the architecture, optimization algorithm, activation functions and the number of convolution filters. With the designed network, we used relatively less training data than other segmentation methods. The direct output of our network, with no further post-processing, resulted in the dice score of ~99 in training and ~95 in validation images in 3D liver segmentation, which is comparable to the state-of-the-art models that used more training images and post-processing. The proposed approach can be easily adapted to other medical image segmentation tasks.
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References
Goodfellow, I., et al.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Szegedy, C., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI (2017)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010)
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2015)
Çiçek, Ö., et al.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2016)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)
Hinton, G.: Neural networks for machine learning. Coursera, [video lectures] (2012)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016
Van Ginneken, B., Heimann, T., Styner, M.: 3D segmentation in the clinic: a grand challenge. In: 3D Segmentation in the Clinic: a Grand Challenge, pp. 7–15 (2007)
Soler, L., et al.: 3D Image reconstruction for comparison of algorithm database: a patient specific anatomical and medical image database (2010)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI (2016)
Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 Fourth International Conference on. IEEE (2016)
Chen, H., et al.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)
Drozdzal, M., et al.: The importance of skip connections in biomedical image segmentation. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer (2016)
Ng, A.: Machine Learning Yearning (2017)
Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016)
Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Pedamonti, D.: Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv preprint arXiv:1804.02763 (2018)
Hu, P., et al.: Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys. Med. Biol. 61(24), 8676 (2016)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
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Mohagheghi, S., Foruzan, A.H., Chen, YW. (2020). Improving the Performance of Deep CNNs in Medical Image Segmentation with Limited Resources. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_5
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