Thrombus Detection in CT Brain Scans using a Convolutional Neural Network

Aneta Lisowska, Erin Beveridge, Keith Muir, Ian Poole

Abstract

Automatic detection and measurement of thrombi may expedite clinical workflow in the treatment planning stage. Nevertheless it is a challenging task on non-contrast computed tomography due to the subtlety of the pathological intensity changes, which are further confounded by the appearance of vascular calcification (common in ageing brains). In this paper we propose a 3D Convolutional Neural Network architecture to detect these subtle signs of stroke. The architecture is designed to exploit contralateral features and anatomical atlas information. We use 122 CT volumes equally split into training and testing to validate our method, achieving a ROC AUC of 0.996 and a Precision-Recall AUC of 0.563 in a voxel-level evaluation. The results are not yet at a level for routine clinical use, but they are encouraging.

References

  1. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., and Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE transactions on medical imaging, 35(5):1207-1216.
  2. Arevalo, J., Gonzalez, F. A., Ramos-Pollan, R., Oliveira, J. L., and Guevara Lopez, M. A. (2015). Convolutional neural networks for mammography mass lesion classification. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pages 797-800. IEEE.
  3. Bandyopadhyay, S. K. (2010). Breast asymmetry-tutorial review. Breast, 9(8).
  4. Brosch, T., Yoo, Y., Tang, L. Y., Li, D. K., Traboulsee, A., and Tam, R. (2015). Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015 , pages 3-11. Springer.
  5. Chawla, M., Sharma, S., Sivaswamy, J., and Kishore, L. (2009). A method for automatic detection and classification of stroke from brain ct images. In Engineering in Medicine and Biology Society, volume 2009, pages 3581-3584.
  6. Ciresan, D., Giusti, A., Gambardella, L. M., and Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems, pages 2843-2851.
  7. Ciresan, D. C., Giusti, A., Gambardella, L. M., and Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8150 LNCS(PART 2):411-418.
  8. (2014). Detection and location of 127 anatomical landmarks in diverse ct datasets. In SPIE Medical Imaging, pages 903415-903415. International Society for Optics and Photonics.
  9. Davis, J. and Goadrich, M. (2006). The relationship between precision-recall and roc curves. In Proceedings of the 23rd International Conference on Machine Learning, ICML'06, pages 233-240.
  10. Doyle, S., Vasseur, F., Dojat, M., and Forbes, F. (2013). Fully automatic brain tumor segmentation from multiple mr sequences using hidden markov fields and variational em. Procs. NCI-MICCAI BraTS, pages 18-22.
  11. Dvorak, P. and Menze, B. (2015). Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. Proceedings of BRATSMICCAI.
  12. Erihov, M., Alpert, S., Kisilev, P., and Hashoul, S. (2015). A cross saliency approach to asymmetry-based tumor detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 636-643. Springer.
  13. Gao, M., Xu, Z., Lu, L., Harrison, A. P., Summers, R. M., and Mollura, D. J. (2016). Multi-label deep regression and unordered pooling for holistic interstitial lung disease pattern detection. In International Workshop on Machine Learning in Medical Imaging, pages 147- 155. Springer.
  14. Geremia, E., Clatz, O., Menze, B. H., Konukoglu, E., Criminisi, A., and Ayache, N. (2011). Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images. NeuroImage, 57(2):378- 390.
  15. Hasan, A., Meziane, F., and Khadim, M. (2016). Automated segmentation of tumours in mri brain scans. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016), pages 55-62. SCITEPRESS.
  16. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H. (2016). Brain tumor segmentation with deep neural networks. Medical Image Analysis.
  17. Huang, X., Cheripelli, B. K., Lloyd, S. M., Kalladka, D., Moreton, F. C., Siddiqui, A., Ford, I., and Muir, K. W. (2015). Alteplase versus tenecteplase for thrombolysis after ischaemic stroke (attest): a phase 2, randomised, open-label, blinded endpoint study. The Lancet Neurology, 14(4):368-376.
  18. Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., and Glocker, B. (2015). Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. Ischemic Stroke Lesion Segmentation, page 13.
  19. Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., and Glocker, B. (2016). Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. arXiv preprint arXiv:1603.05959.
  20. Li, W., Jia, F., and Hu, Q. (2015). Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Journal of Computer and Communications, 3(11):146.
  21. Liswoska, A., Beveridge, E., and Poole, I. (2016). False positive reduction methods applied to dense vessel detection in brain ct images. Poster presented at Medical Imaging Summer School, Favigana, Italy.
  22. O'Neil, A., Murphy, S., and Poole, I. (2015). Anatomical landmark detection in ct data by learned atlas location autocontext. In MIUA, pages 189-194.
  23. Payan, A. and Montana, G. (2015). Predicting alzheimer's disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506.
  24. Pereira, S., Pinto, A., Alves, V., and Silva, C. A. (2015). Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri. In International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pages 131-143. Springer.
  25. Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., and Nielsen, M. (2013). Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013 , pages 246-253. Springer.
  26. Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., and Fenster, A. (2013). Fast globally optimal segmentation of 3d prostate mri with axial symmetry prior. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 198-205. Springer.
  27. Rao, A., Ledig, C., Newcombe, V., Menon, D., and Rueckert, D. (2014). Contusion segmentation from subjects with traumatic brain injury: a random forest framework. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pages 333-336. IEEE.
  28. Rao, V., Sarabi, M. S., and Jaiswal, A. (2015). Brain tumor segmentation with deep learning. Proceedings of BRATS-MICCAI.
  29. Riedel, C. H., Zimmermann, P., Jensen-Kondering, U., Stingele, R., Deuschl, G., and Jansen, O. (2011). The importance of size successful recanalization by intravenous thrombolysis in acute anterior stroke depends on thrombus length. Stroke, 42(6):1775-1777.
  30. Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., and Summers, R. M. (2014). A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014, pages 520-527. Springer.
  31. Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., and Summers, R. M. (2016). Deep convolutional neural networks for computeraided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5):1285-1298.
  32. Urban, G., Bendszus, M., Hamprecht, F., and Kleesiek, J. (2014). Multi-modal brain tumor segmentation using deep convolutional neural networks. Proceedings of BRATS-MICCAI.
  33. Wang, J., MacKenzie, J. D., Ramachandran, R., and Chen, D. Z. (2015). Neutrophils identification by deep learning and voronoi diagram of clusters. In Medical Image Computing and Computer-Assisted InterventionMICCAI 2015, pages 226-233. Springer.
  34. Wardlaw, J. M., Muir, K. W., Macleod, M.-J., Weir, C., McVerry, F., Carpenter, T., Shuler, K., Thomas, R., Acheampong, P., Dani, K., et al. (2013). Clinical relevance and practical implications of trials of perfusion and angiographic imaging in patients with acute ischaemic stroke: a multicentre cohort imaging study. Journal of Neurology, Neurosurgery & Psychiatry, pages jnnp-2012.
  35. Wolterink, J. M., Leiner, T., Viergever, M. A., and Is?gum, I. (2015). Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In Medical Image Computing and ComputerAssisted Intervention-MICCAI 2015 , pages 589-596. Springer.
  36. Yang, D., Zhang, S., Yan, Z., Tan, C., Li, K., and Metaxas, D. (2015). Automated anatomical landmark detection on distal femur surface using convolutional neural network. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pages 17-21. IEEE.
  37. Zhao, L., Wu, W., and Corso, J. J. (2013). Semi-automatic brain tumor segmentation by constrained mrfs using structural trajectories. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 567-575. Springer.
  38. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O., Das, T., Jena, R., and Price, S. (2012). Decision forests for tissuespecific segmentation of high-grade gliomas in multichannel mr. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 369-376. Springer.
Download


Paper Citation


in Harvard Style

Lisowska A., Beveridge E., Muir K. and Poole I. (2017). Thrombus Detection in CT Brain Scans using a Convolutional Neural Network . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 24-33. DOI: 10.5220/0006114600240033


in Bibtex Style

@conference{bioimaging17,
author={Aneta Lisowska and Erin Beveridge and Keith Muir and Ian Poole},
title={Thrombus Detection in CT Brain Scans using a Convolutional Neural Network},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={24-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006114600240033},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - Thrombus Detection in CT Brain Scans using a Convolutional Neural Network
SN - 978-989-758-215-8
AU - Lisowska A.
AU - Beveridge E.
AU - Muir K.
AU - Poole I.
PY - 2017
SP - 24
EP - 33
DO - 10.5220/0006114600240033