Advertisement

Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation

  • Huan QiEmail author
  • Sally Collins
  • J. Alison Noble
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Accurate localization of structural abnormalities is a precursor for image-based prenatal assessment of adverse conditions. For clinical screening and diagnosis of abnormally invasive placenta (AIP), a life-threatening obstetric condition, qualitative and quantitative analysis of ultrasonic patterns correlated to placental lesions such as placental lacunae (PL) is challenging and time-consuming to perform even for experienced sonographers. There is a need for automated placental lesion localization that does not rely on expensive human annotations such as detailed manual segmentation of anatomical structures. In this paper, we investigate PL localization in 2D placental ultrasound images. First, we demonstrate the effectiveness of generating confidence maps from weak dot annotations in localizing PL as an alternative to expensive manual segmentation. Then we propose a layer aggregation structure based on iterative deep aggregation (IDA) for PL localization. Models with this structure were evaluated with 10-fold cross-validations on an AIP database (containing 3,440 images with 9,618 labelled PL from 23 AIP and 11 non-AIP participants). Experimental results demonstrate that the model with the proposed structure yielded the highest mean average precision (mAP = 35.7%), surpassing all other baseline models (32.6%, 32.2%, 29.7%). We argue that features from shallower stages can contribute to PL localization more effectively using the proposed structure. To our knowledge, this is the first successful application of machine learning to placental lesion analysis and has the potential to be adapted for other clinical scenarios in breast, liver, and prostate cancer imaging.

Notes

Acknowledgements

Huan Qi is supported by a China Scholarship Council doctoral research fund (grant No. 201608060317). The NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development Human Placenta Project UO1 HD 087209, EPSRC grant EP/M013774/1, and ERC-ADG-2015 694581 are also acknowledged.

Supplementary material

473975_1_En_102_MOESM1_ESM.pdf (425 kb)
Supplementary material 1 (pdf 424 KB)

References

  1. 1.
    Jauniaux, E., et al.: The placenta accreta spectrum: pathophysiology and evidence-based anatomy for prenatal ultrasound imaging. AJOG 218(1), 75–87 (2018)CrossRefGoogle Scholar
  2. 2.
    Fitzpatrick, K., et al.: The management and outcomes of placenta accreta, increta, and percreta in the UK: a population-based descriptive study. BJOG 121(1), 62–71 (2014)CrossRefGoogle Scholar
  3. 3.
    Thurn, L., et al.: Abnormally invasive placenta - prevalence, risk factors and antenatal suspicion: results from a large population-based pregnancy cohort study in the Nordic countries. BJOG 123(8), 1348–1355 (2016)CrossRefGoogle Scholar
  4. 4.
    Collins, S., et al.: Proposal for standardized ultrasound descriptors of abnormally invasive placenta (AIP). Ultrasound Obstet. Gynecol. 47(3), 271–275 (2016)CrossRefGoogle Scholar
  5. 5.
    Xie, W., et al.: Microscopy cell counting with fully convolutional regression networks. In: MICCAI Deep Learning Workshop (2015)Google Scholar
  6. 6.
    Cao, Z., et al.: Realtime multi-person 2d pose estimation using part affinity fields. In: IEEE CVPR (2017)Google Scholar
  7. 7.
    Achanta, R., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE T-PAMI 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhou, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Deep supervision for pancreatic cyst segmentation in abdominal CT scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 222–230. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_26CrossRefGoogle Scholar
  9. 9.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  10. 10.
    Xie, S., et al.: Holistically-nested edge detection. In: IEEE ICCV (2015)Google Scholar
  11. 11.
    Lin, T.Y., et al.: Feature pyramid networks for object detection. In: IEEE CVPR (2017)Google Scholar
  12. 12.
    Yu, F., et al.: Deep layer aggregation. In: IEEE CVPR (2018)Google Scholar
  13. 13.
    Collins, S., et al.: Influence of power doppler gain setting on virtual organ computer-aided analysis indices in vivo: can use of the individual sub-noise gain level optimize information? Ultrasound Obstet. Gynecol. 40(1), 75–80 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Biomedical Engineering (IBME)University of OxfordOxfordUK
  2. 2.Nuffield Department of Women’s and Reproductive HealthUniversity of OxfordOxfordUK

Personalised recommendations