Abstract
Stacked autoencoder was used for the segmentation of trabeculas from bone marrow histological images derived from patients after hip joints arthroplasty. Additional filtering of areas smaller than 20000 pixels is necessary. The method has 95% efficiency. Proposed stacked autoencoder processes input images without special intervention automatically that is the main advantage of unsupervised learning over the supervised learning.
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Ahmad, M., Yang, J., Ai, D., Qadri, S.F., Wang, Y.: Deep-stacked auto encoder for liver segmentation. In: Wang, Y., et al. (eds.) Advances in Image and Graphics Technologies, IGTA 2017, vol. 757, pp. 243–251. Springer, Singapore (2018)
Domagala, W., Chosia, M., Urasinska, E.: Atlas of histopathology. Wydawnictwo Lekarskie PZWL (2007)
Kumar, B., Abbas, A.K., Aster, J.: Robbins Basic Pathology. Elsevier, Philadelphia (2013)
Kumar, V., Abbas, A.K., Aster, J.C.: Robbins & Cotran Pathologic Basis of Disease. Elsevier, Philadelphia (2015)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256, May 2010
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lei, Y., Yuan, W., Wang, H., Wenhu, Y., Bo, W.: A skin segmentation algorithm based on stacked autoencoders. IEEE Trans. Multimedia 19(4), 740–749 (2017)
Oszutowska-Mazurek, D., Knap, O.: The use of deep learning for segmentation of bone marrow histological images. In: Silhavy, R., Senkerik, R., Kominkova, O.Z., Prokopova, Z., Silhavy, P. (eds.) Artificial Intelligence Trends in Intelligent Systems, CSOC 2017, vol. 573, pp. 466–473. Springer International Publishing, Cham (2017)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19(1545–4274 (Electronic)), 221–248 (2017)
Sobotta, J.: Histology. Urban & Schwarzenberg, Baltimore (1983)
Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., Yang, L.: Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, vol. 9351, pp. 383–390. Springer International Publishing, Cham (2015)
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)
Zeng, X., Ricardo Leung, M., Zeev-Ben-Mordehai, T., Xu, M.: A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation, June 2017
Acknowledgement
This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.
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Oszutowska-Mazurek, D., Mazurek, P., Knap, O. (2019). Stacked Autoencoder for Segmentation of Bone Marrow Histological Images. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_42
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DOI: https://doi.org/10.1007/978-3-319-91189-2_42
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