Abstract
Computer-aided detection (CAD) can play a major role in diagnosing pulmonary embolism (PE) at CT pulmonary angiography (CTPA). However, despite their demonstrated utility, to achieve a clinically acceptable sensitivity, existing PE CAD systems generate a high number of false positives, imposing extra burdens on radiologists to adjudicate these superfluous CAD findings. In this study, we investigate the feasibility of convolutional neural networks (CNNs) as an effective mechanism for eliminating false positives. A critical issue in successfully utilizing CNNs for detecting an object in 3D images is to develop a “right” image representation for the object. Toward this end, we have developed a vessel-aligned multi-planar image representation of emboli. Our image representation offers three advantages: (1) efficiency and compactness—concisely summarizing the 3D contextual information around an embolus in only 2 image channels, (2) consistency—automatically aligning the embolus in the 2-channel images according to the orientation of the affected vessel, and (3) expandability—naturally supporting data augmentation for training CNNs. We have evaluated our CAD approach using 121 CTPA datasets with a total of 326 emboli, achieving a sensitivity of 83% at 2 false positives per volume. This performance is superior to the best performing CAD system in the literature, which achieves a sensitivity of 71% at the same level of false positives. We have further evaluated our system using the entire 20 CTPA test datasets from the PE challenge. Our system outperforms the winning system from the challenge at 0mm localization error but is outperformed by it at 2mm and 5mm localization errors. In our view, the performance at 0mm localization error is more important than those at 2mm and 5mm localization errors.
Chapter PDF
Similar content being viewed by others
Keywords
References
Bouma, H., Sonnemans, J.J., Vilanova, A., Gerritsen, F.A.: Automatic detection of pulmonary embolism in cta images. IEEE Transactions on Medical Imaging 28(8), 1223–1230 (2009)
Das, M., Mühlenbruch, G., Helm, A., Bakai, A., Salganicoff, M., Stanzel, S., Liang, J., Wolf, M., Günther, R.W., Wildberger, J.E.: Computer-aided detection of pulmonary embolism: influence on radiologists detection performance with respect to vessel segments. European Radiology 18(7), 1350–1355 (2008)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Liang, J., Bi, J.: Computer aided detection of pulmonary embolism with tobogganing and mutiple instance classification in CT pulmonary angiography. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 630–641. Springer, Heidelberg (2007)
Liang, J., Bi, J.: Local characteristic features for computer aided detection of pulmonary embolism in ct angiography. In: Proceedings of the First MICCAI Workshop on Pulmonary Image Analysis, pp. 263–272 (2008)
Özkan, H., Osman, O., Şahin, S., Boz, A.F.: A novel method for pulmonary embolism detection in cta images. Computer Methods and Programs in Biomedicine 113(3), 757–766 (2014)
Park, S.C., Chapman, B.E., Zheng, B.: A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: Preliminary investigation. IEEE Transactions on Biomedical Engineering 58(6), 1519–1527 (2011)
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013)
Roth, H.R., et al.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 520–527. Springer, Heidelberg (2014)
Wang, X., Song, X., Chapman, B.E., Zheng, B.: Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm. In: SPIE Medical Imaging, pp. 83152U–83152U. International Society for Optics and Photonics (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tajbakhsh, N., Gotway, M.B., Liang, J. (2015). Computer-Aided Pulmonary Embolism Detection Using a Novel Vessel-Aligned Multi-planar Image Representation and Convolutional Neural Networks. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-24571-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24570-6
Online ISBN: 978-3-319-24571-3
eBook Packages: Computer ScienceComputer Science (R0)