CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

  • Bo Zhou
  • Adam P. HarrisonEmail author
  • Jiawen Yao
  • Chi-Tung Cheng
  • Jing Xiao
  • Chien-Hung Liao
  • Le Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)


As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital picture archiving and communication systems (PACSs). This is the focus of our work, where we present a principled data curation tool to extract multi-phase computed tomography (CT) liver studies and identify each scan’s phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institutional partners that are text mined using simple rules from DICOM tags. We train a deep learning system, using a customized and streamlined 3D squeeze and excitation (SE) architecture, to identify non-contrast, arterial, venous, and delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a dataset of 43K scans of 7680 patient imaging studies demonstrate that our 3DSE architecture, armed with our aggregated loss, can achieve a mean F1 of 0.977 and can correctly harvest up to \(92.7\%\) of studies, which significantly outperforms the text-mined and standard-loss approach, and also outperforms other, and more complex, model architectures.


Data curation PACS Dynamic CT Phase recognition 

Supplementary material

490967_1_En_16_MOESM1_ESM.pdf (499 kb)
Supplementary material 1 (pdf 499 KB)


  1. 1.
    Litjens, G.J.S., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  2. 2.
    Zhou, B., Lin, X., Eck, B., Hou, J., Wilson, D.: Generation of virtual dual energy images from standard single-shot radiographs using multi-scale and conditional adversarial network. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 298–313. Springer, Cham (2019). Scholar
  3. 3.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE CVPR, pp. 248–255 (2009)Google Scholar
  4. 4.
    Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning: Whitepaper from the 2016 C-MIMI meeting dataset session. J. Digital Imaging 30(4), 392–399 (2017)Google Scholar
  5. 5.
    Harvey, H., Glocker, B.: A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert, E.R., Morozov, S., Algra, P.R. (eds.) Artificial Intelligence in Medical Imaging, pp. 61–72. Springer, Cham (2019). Scholar
  6. 6.
    Yan, K., Wang, X., Lu, L., Summers, R.M.: Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)CrossRefGoogle Scholar
  7. 7.
    Zhou, B., Chen, A., Crawford, R., Dogdas, B., Goldmarcher, G.: A progressively-trained scale-invariant and boundary-aware deep neural network for the automatic 3D segmentation of lung lesions. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE (2019)Google Scholar
  8. 8.
    Irvin, J., Rajpurkar, P., et al.: Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: AAAI (2019)Google Scholar
  9. 9.
    Peng, Y., Wang, X., Lu, L., Bagheri, M., Summers, R., Lu, Z.: NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA Jt Summits Transl. Sci. Proc. 2018, 188–196 (2018)Google Scholar
  10. 10.
    Burrowes, D.P., Medellin, A., Harris, A.C., Milot, L., Wilson, S.R.: Contrast-enhanced us approach to the diagnosis of focal liver masses. RadioGraphics 37(5), 1388–1400 (2017)CrossRefGoogle Scholar
  11. 11.
    Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)Google Scholar
  12. 12.
    Gueld, M.O., et al.: Quality of DICOM header information for image categorization. In: Proceedings of SPIE Medical Imaging (2002)Google Scholar
  13. 13.
    Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3D residual networks for action recognition. In: IEEE CVPR, pp. 3154–3160 (2017)Google Scholar
  14. 14.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)Google Scholar
  15. 15.
    Yeh, A.: More accurate tests for the statistical significance of result differences. In: Proceedings of the 18th Conference on Computational Linguistics - Volume 2. COLING 2000, Stroudsburg, PA, USA, pp. 947–953 (2000)Google Scholar
  16. 16.
    Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Zhao, G., Zhou, B., Wang, K., Jiang, R., Xu, M.: Respond-CAM: analyzing deep models for 3D imaging data by visualizations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 485–492. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bo Zhou
    • 1
    • 2
  • Adam P. Harrison
    • 2
    Email author
  • Jiawen Yao
    • 2
  • Chi-Tung Cheng
    • 4
  • Jing Xiao
    • 3
  • Chien-Hung Liao
    • 4
  • Le Lu
    • 2
  1. 1.Biomedical EngineeringYale UniversityNew HavenUSA
  2. 2.PAII Inc.BethesdaUSA
  3. 3.PingAn TechnologyShenzhenChina
  4. 4.Chang Gung Memorial HospitalLinkouROC

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