Advertisement

Beyond Retinal Layers: A Large Blob Detection for Subretinal Fluid Segmentation in SD-OCT Images

  • Zexuan Ji
  • Qiang Chen
  • Menglin Wu
  • Sijie Niu
  • Wen Fan
  • Songtao Yuan
  • Quansen Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Purpose: To automatically segment neurosensory retinal detachment (NRD)-associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images by constructing a Hessian-based Aggregate generalized Laplacian of Gaussian algorithm without the use of retinal layer segmentation. Methods: The B-scan is first filtered into small blob candidate regions based on local convexity by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. Two Hessian-based regional features are extracted based on the aggregate response map. Pooling with regional intensity, the feature vectors are fed into an unsupervised clustering algorithm. By voting the blob candidates into the superpixels, the initial subretinal fluid regions are obtained. Finally, an active contour with narrowband implementation is utilized to obtain integrated segmentations. Results: The testing data set with 23 longitudinal SD-OCT cube scans from 12 eyes of 12 patients are used to evaluate the proposed algorithm. Comparing with two independent experts’ manual segmentations, our algorithm obtained a mean true positive volume fraction 95.15%, positive predicative value 93.65% and dice similarity coefficient 94.35%, respectively. Conclusions: Without retinal layer segmentation, the proposed algorithm can produce higher segmentation accuracy comparing with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable subretinal fluid segmentations for NRD from SD-OCT images.

Keywords

Spectral domain optical coherence tomography Subretinal fluid segmentation Neurosensory retinal detachment Blob segmentation 

References

  1. 1.
    Teke, M.Y., et al.: Comparison of autofluorescence and optical coherence tomography findings in acute and chronic central serous chorioretinopathy. Int. J. Ophthalmol. 7(2), 350 (2014)MathSciNetGoogle Scholar
  2. 2.
    Wang, J., et al.: Automated volumetric segmentation of retinal fluid on optical coherence tomography. BOE 7(4), 1577–1589 (2016)Google Scholar
  3. 3.
    Wu, M., et al.: Automatic Subretinal fluid segmentation of retinal SD-OCT images with neurosensory retinal detachment guided by enface fundus imaging. IEEE-TBE 65(1), 87–95 (2018)Google Scholar
  4. 4.
    Zheng, Y., et al.: Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina. Am. J. Ophthalmol. 155(2), 277–286 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, T., et al.: Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images. Info. Sci. 358, 92–111 (2016)CrossRefGoogle Scholar
  6. 6.
    Lang, A., et al.: Automatic segmentation of microcystic macular edema in OCT. BOE 6(1), 155–169 (2015)Google Scholar
  7. 7.
    Xu, Y., et al.: Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy. BOE 8(9), 4061–4076 (2017)Google Scholar
  8. 8.
    Kong, H., et al.: A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE-TCy 43(6), 1719–1733 (2013)Google Scholar
  9. 9.
    Zhang, M., et al.: Small blob identification in medical images using regional features from optimum scale. IEEE-TBE 62(4), 1051–1062 (2015)Google Scholar
  10. 10.
    Zhang, M., et al.: Efficient small blob detection based on local convexity, intensity and shape information. IEEE-TMI 35(4), 1127–1137 (2016)Google Scholar
  11. 11.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  12. 12.
    Ji, Z., et al.: Active contours driven by local likelihood image fitting energy for image segmentation. Info. Sci. 301, 285–304 (2015)CrossRefGoogle Scholar
  13. 13.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0056195CrossRefGoogle Scholar
  14. 14.
    Achanta, R.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE-TPAMI 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  15. 15.
    Xu, X., et al.: Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data. IEEE-TMI 34(7), 1616–1623 (2015)Google Scholar
  16. 16.
    Wu, M., et al.: Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy. BOE 8(9), 4257–4274 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Computer Science and TechnologyNanjing Tech UniversityNanjingChina
  3. 3.School of Information Science and EngineeringUniversity of JinanJinanChina
  4. 4.Department of OphthalmologyThe First Affiliated Hospital with Nanjing Medical UniversityNanjingChina

Personalised recommendations