Abstract
Automated methods for detecting pulmonary embolisms (PEs) on CT pulmonary angiography (CTPA) images are of high demand. Existing methods typically employ separate steps for PE candidate detection and false positive removal, without considering the ability of the other step. As a result, most existing methods usually suffer from a high false positive rate in order to achieve an acceptable sensitivity. This study presents an end-to-end trainable convolutional neural network (CNN) where the two steps are optimized jointly. The proposed CNN consists of three concatenated subnets: (1) a novel 3D candidate proposal network for detecting cubes containing suspected PEs, (2) a 3D spatial transformation subnet for generating fixed-sized vessel-aligned image representation for candidates, and (3) a 2D classification network which takes the three cross-sections of the transformed cubes as input and eliminates false positives. We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78.9%, 80.7% and 80.7% at 2 false positives per volume at 0 mm, 2 mm and 5 mm localization error, which is superior to the state-of-the-art methods. We have further evaluated our system on our own dataset consisting of 129 CTPA data with a total of 269 emboli. Our system achieves a sensitivity of 63.2%, 78.9% and 86.8% at 2 false positives per volume at 0 mm, 2 mm and 5 mm localization error.
This work was supported by the National Natural Science Foundation of China (61502188), the Hubei Provincial Natural Science Foundation (ZRMS2017000375), the Wuhan Science and Technology Bureau under Award (2017010201010111), the Fundamental Research Funds for the Central Universities (2019kfyRCPY118) and the Program for HUST Acadamic Frontier Youth Team.
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References
CAD-PE challenge. http://www.cad-pe.org/
Araoz, P.A., et al.: Pulmonary embolism: prognostic CT findings. Radiology 242(3), 889–897 (2007)
Bouma, H., et al.: Automatic detection of pulmonary embolism in CTA images. IEEE Trans. Med. Imaging 28(8), 1223–1230 (2009)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jaderberg, M., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)
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). https://doi.org/10.1007/978-3-540-73273-0_52
Liao, F., et al.: Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network. In: IEEE Transactions on Neural Networks and Learning Systems (2019)
Masoudi, M., et al.: A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism. Sci. Data 5, 1–9 (2018)
Masutani, Y., et al.: Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans. Med. Imaging 21(12), 1517–1523 (2002)
Ren, S., et al.: Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)
Tajbakhsh, N., Gotway, M.B., Liang, J.: 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.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 62–69. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_8
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Lin, Y. et al. (2019). Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_31
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DOI: https://doi.org/10.1007/978-3-030-32251-9_31
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