Abstract
The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis. Recently, graph-based methods with a global optimization property have been developed and optimized for various medical imaging applications. Despite their widespread use, these require human experts to design transformations, image features, surface smoothness priors, and re-design for a different tissue, organ or imaging modality. Here, we propose a Deep Learning based approach for segmentation of the surfaces in volumetric medical images, by learning the essential features and transformations from training data, without any human expert intervention. We employ a regional approach to learn the local surface profiles. The proposed approach was evaluated on simultaneous intraretinal layer segmentation of optical coherence tomography (OCT) images of normal retinas and retinas affected by age related macular degeneration (AMD). The proposed approach was validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The experiments showed statistically significant improvement in accuracy for our approach compared to state-of-the-art graph based optimal surface segmentation with convex priors (G-OSC). A single Convolutional Neural Network (CNN) was used to learn the surfaces for both normal and diseased images. The mean unsigned surface positioning errors obtained by G-OSC method 2.31 voxels (\(95\%\) CI 2.02-2.60 voxels) was improved to 1.27 voxels (\(95\%\) CI 1.14-1.40 voxels) using our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB memory, which is much faster than the 2837.46 s and 6.87 GB memory required by the G-OSC method on the same computer system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Challenge, M.B.G.: Multimodal brain tumor segmentation benchmark: change detection. http://braintumorsegmentation.org/. Accessed 5 Nov 2016
Farsiu, S., Chiu, S.J., O’Connell, R.V., Folgar, F.A., Yuan, E., Izatt, J.A., Toth, C.A.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1), 162–172 (2014)
Kaggle: diabetic retinopathy detection. http://www.kaggle.com/c/diabetic-retinopathy-detection/. Accessed 15 July 2016
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)
Lee, K., Garvin, M., Russell, S., Sonka, M., Abràmoff, M.: Automated intraretinal layer segmentation of 3-d macular oct scans using a multiscale graph search. Invest. Ophthalmol. Vis. Sci. 51(13), 1767 (2010)
Shah, A., Bai, J., Hu, Z., Sadda, S., Wu, X.: Multiple surface segmentation using truncated convex priors. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 97–104. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_12
Song, Q., Bai, J., Garvin, M.K., Sonka, M., Buatti, J.M., Wu, X.: Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imag. 32(2), 376–386 (2013)
Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic, M.: Intra-retinal layer segmentation in optical coherence tomography using an active contour approach. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 649–656. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04271-3_79
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shah, A., Abramoff, M.D., Wu, X. (2017). Simultaneous Multiple Surface Segmentation Using Deep Learning. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)