Medical Image Analysis with Semantic Segmentation and Active Learning

C.I. Saidu; L. Csato

doi:10.24193/subbi.2019.1.03

C.I. Saidu Computer Science Department, African University of Science and Technology, Airport Road, 10 km, Abuja, Nigeria
L. Csato Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj, Romania

DOI: https://doi.org/10.24193/subbi.2019.1.03

Abstract

We address object detection using semantic segmentation and apply it for prostate detection in an MRI data-set. Our detection pipeline uses first a segmentation step followed by a classifier with a convolutional neural network (CNN). Since the segmentation provides a set of unbalanced data-sets – where a high accuracy is difficult to obtain – we leverage the prospect of improving detection accuracy using a Bayesian treatment of deep networks and the possibility of better exploiting the data using active learning. The resulting algorithm is both adaptive and data-efficient: by assuming that from a large pool of data only a few are segmented, the active learning module of the algorithm finds the image that improves most detection accuracy. We test our algorithm on a prostate medical image data-set and show that the active learning-based algorithm performs well in the prostate detection class. The resulting system is invariant to translations within the image and the results show improvements when using the pipeline that includes active learning and CNNs.

References

[1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´ egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.
[2] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, 34(11):2274–2282, 2012.
[3] C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York, NY, USA, 1995. ISBN 0198538642.
[4] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, 2002.
[5] D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 2843–2851. Curran Associates, Inc., 2012.
[6] S. Ertekin, J. Huang, L. Bottou, and L. Giles. Learning on the border: active learning in imbalanced data classification. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM ’07, pages 127–136, New York, NY, USA, 2007. ACM. ISBN 978-1-59593-803-9. doi: 10.1145/1321440.1321461.
[7] A. Fathi, M. F. Balcan, X. Ren, and J. M. Rehg. Combining self training and active learning for video segmentation. In Proceedings of the British Machine Vision Conference, pages 78.1–78.11. BMVA Press, 2011. ISBN 1-901725-43-X.
[8] Y. Gal. Uncertainty in Deep Learning. PhD thesis, University of Cambridge, 2016.
[9] Y. Gal, R. Islam, and Z. Ghahramani. Deep Bayesian active learning with image data. In D. Precup and Y. W. Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1183–1192, International Convention Centre, Sydney, Australia, 06–11 Aug 2017. PMLR.
[10] S. Ghose, A. Oliver, R. Mart´ı, X. Lladó, J. C. Vilanova, J. Freixenet, J. Mitra, D. Sidib´e, and F. Meriaudeau. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Methods Programs Biomed., 108(1):262–287, 2012. ISSN 0169-2607. doi: 10.1016/j.cmpb.2012.04.006.
[11] R. Girshick. Fast r-cnn. In The IEEE International Conference on Computer Vision (ICCV), December 2015.
[12] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
[13] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016.
[14] S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1–8. IEEE, 2009.
[15] K. He, G. Gkioxari, P. Dollár, and R. B. Girshick. Mask R-CNN. CoRR, abs/1703.06870, 2017.
[16] N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel. Bayesian active learning for classification and preference learning. ArXiv e-prints, 2011.
[17] Z. Hu, Q. Zou, and Q. Li. Watershed superpixel. 2015 IEEE International Conference on Image Processing (ICIP), pages 349–353, 2015. doi: 10.1109/ICIP.2015.7350818.
[18] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
[19] G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman, and A. Madabhushi. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2):359–373, 2 2014. ISSN 1361-8415.
[20] D. J. C. MacKay. Information Theory, Inference & Learning Algorithms. Cambridge University Press, New York, NY, USA, 2002. ISBN 0521642981.
[21] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 91–99. Curran Associates, Inc., 2015.
[22] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015.
[23] B. Scholkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA, 2001. ISBN 0262194759.
[24] B. Schölkopf, A. Smola, and K.-R. Müller. Kernel principal component analysis. In B. Schölkopf, C. J. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 327–352. MIT Press, 1999.
[25] B. Settles. Active Learning Literature Survey. Mach. Learn., 15(2):201–221, 2010. ISSN 00483931. doi: 10.1.1.167.4245.
[26] B. Settles, M. Craven, and S. Ray. Multiple-instance active learning. In Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference, volume 20, pages 1289–1296, 2008. ISBN 160560352X.
[27] E. Shelhamer, J. Long, and T. Darrell. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 39(4):640–651, Apr. 2014. ISSN 0162-8828. doi: 10.1109/TPAMI.2016.2572683.
[28] M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis, and Machine Vision. Thomson-Engineering, 2007. ISBN 049508252X.
[29] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15(1):1929–1958, Jan. 2014. ISSN 1532-4435.
[30] A. Vezhnevets, V. Ferrari, and J. Buhmann. Weakly Supervised Semantic Segmentation with Multi Image Model. Proc. Int’l Conf. Comput. Vis., 2011.
[31] A. Vezhnevets, J. M. Buhmann, and V. Ferrari. Active learning for semantic segmentation with expected change. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pages 3162–3169, 2012. ISSN 10636919.