Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture
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Published:
Apr 1, 2019
Keywords:
Deep Learning, Biomedical Engineering, 3D Imaging, 3D CNN, CT Scan, Sphenoid Sinus, Automatic Segmentation.
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Abstract
Today, researchers are increasingly using manual, semi-automatic, and automatic segmentation techniques to delimit or extract organs from medical images. Deep learning algorithms are increasingly being used in the area of medical imaging analysis. In comparison to traditional methods, these algorithms are more efficient to obtain compact information, which considerably enhances the quality of medical image analysis system. In this paper, we present a new method to fully automatic segmentation of the sphenoid sinus using a 3D (convolutional neural network). The scarcity of medical data initially forced us through this study to use a 3D CNN model learned on a small data set. To make our method fully automatic, preprocessing and post processing are automated with extraction techniques and mathematical morphologies. The proposed tool is compared to a semi-automatic method and manual deductions performed by a specialist. Preliminary results from CT volumes appear very promising.
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Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture. (2019). Medical Technologies Journal, 3(1), 334-346. https://medtech.ichsmt.org/index.php/MTJ/article/view/167
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Medical technologies
How to Cite
Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture. (2019). Medical Technologies Journal, 3(1), 334-346. https://medtech.ichsmt.org/index.php/MTJ/article/view/167
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References
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[3]. Stokovic, N. Trkulja, V. Dumic-Cule, V. Cukovic-Bagic, I. T.L.S Vukicevic, T.L.S. & Grgurevic, L. (2015) Sphenoid sinus types, dimensions and relationship with surrounding structures Annals of Anatomy (2015), doi: 10.1016/j.aanat.2015.02.013 PMid:25843780
[4]. Burke M.C., Taheri, R, Bhojwani, R & Singh, A (2015) A Practical Approach to the Imaging Interpretation of Sphenoid Sinus Pathology Current Problems in Diagnostic, http://dx.doi.org/10.10677j.cpradiol.2015.02.002
[5]. Hacl, A. Costa, A.L.F. Oliveira, J.M. Tucunduva, M.J. Girondi, J.R, Raphaelli, A.C. & Scocate, N. (2016) Three-dimensional volumetric analysis of frontal sinus using medical software. Journal of Forensic Radiology and Imaging https://doi.org/10.1016/j.jofri.2017.08.004
[6]. Wu, H.B. Zhu, L. Yuan, H.S. & Hou, C. (2011) Surgical measurement to sphe¬noid sinus for the Chinese in Asia based on CT using sagittal reconstruction images, Eur Arch Otorhinolaryngol (2011) 268 :2)1 2)6. https://doi.org/10.1007/s00405-010-1373-1 PMid:20857131
[7]. Guldnerc, C. Pistorius, S. Diogo, I. Bien, S. Sesterhenn, A.& Werner, J. (2012) Analysis of pneumatization and neurovascular structures of the sphenoid sinus using cone-beam tomography (cbt)Acta. Radiol., vol. 53, no. 2, pp. 214-9, 2012 https://doi.org/10.1258/ar.2011.110381 PMid:22383784
[8]. Auffret, M. Garetier, M. Diallo I. Aho, S. & Ben Salem, D. (2016) Contribution of the computed tomography of the anatomical aspects of the sphenoid sinuses to forensic identification J. Neuroradiol., vol. 43, no. 6, pp. 404414, 2016 https://doi.org/10.1016/j.neurad.2016.03.007 PMid:27083691
[9]. Uthman, A.T. AL-Rawi, N.H. Al-Naaimi,A.S. Tawfeeq A.S. & Suhail E.H. (2009)Evaluation of frontal sinus and skull measurements using spiral CT scan¬ning: An aid in unknown person identification, Forensic Science International 197 (2010) 124.e1 124.e7 https://doi.org/10.1016/j.forsciint.2009.12.064 PMid:20097024
[10]. Kawari, Y. Fukushima, K. Ogawa,T. Nishizaki,K. Gunduz,M. Fujimoto, M.& Yu Masuda (1999)Volume Quantification of Healthy Paranasal Cavity by Three-Dimensional CT Imaging Acta Otolaryngol (Stockh) 1999; Suppl 540: 45¬49 https://doi.org/10.1080/00016489950181198
[11]. Ahirwar, A. (2013) Study of Techniques used for Medical Image Segmen¬tation and Computation of Statistical Test for Region Classification of Brain MRII.J. Information Technology and Computer Science, 2013, 05, 44 53 https://doi.org/10.5815/ijitcs.2013.05.06
[12]. Shen, D. Wu, G. & Suk, H.(2017) Annu. Rev. Biomed. Eng. 2017. 19:22148 (The Annual Review of Biomedical Engineering is online at bioeng.annualreviews.org) https://doi.org/10.1146/annurev-bioeng-071516-044442 PMid:28301734 PMCid:PMC5479722
[13]. Srinivas, S. Sarvadevabhatla, R.K. Mopuri, K.R. Prabhu, N. Kruthiventi, S.S.S. & Babu R.V. (2017) Introduction to Deep Convolutional Neural Nets for Com¬puter Vision Deep Learning for Medical Image Analysis -2017, Pages 25-52 https://doi.org/10.1016/B978-0-12-810408-8.00003-1
[14]. Kamnitsas, K. Ledig, C. Newcombe, V.F.J. Simpson, J.P. Kane, A.D. Menon, D.K. Rueckert, D. & Glocker, B. (2015) Efficient multi-scale 3D CNN with fully connected crf for accurate brain lesion segmentationproceeding of ISLES challenge, MICCAI 2015
[15]. Jani, A. Savsani, V. & Pandya A. (2017) 3D Affine Registration using Teaching Learning Based Optimization, 3D Research Center, Kwangwoon Univer¬sity and Springer 2013https://doi.org/10.1007/3DRes.03(2013)2
[16]. Kamnitsas, K. Ferrante, E. Parisot, S. Ledig, C. Nori, A. Criminisi, A Rueckert, D. & Glocker, B. (2016) DeepMedic for Brain Tumor Segmentation Biomedical Image Analysis Group https://doi.org/10.1007/978-3-319-55524-9_14
[17]. Simonyan, K. & Zisserman, A. (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
[18]. Prionas, ND. Ray, S. & Boone, JM. (2011) Volume assessment accuracy in computed tomography: a phantom study J Appl Clin Med Phys 2011; 11(2):3037. https://doi.org/10.1120/jacmp.v11i2.3037 PMCid:PMC5719953
[19]. Wafa, B., & A. Moussaoui. A review on methods to estimate a CT from MRI data in the context of MRI-alone RT. Medical Technologies Journal, Vol. 2, no. 1, Mar. 2018, pp. 150-178. https://doi.org/10.26415/2572-004X-vol2iss1p150-178
[20]. Rachida, Z., A. Belaid, & Salem DB. A segmentation method of skin MRI 3D high resolution in vivo. Medical Technologies Journal, Vol. 2, no. 3, Sept. 2018, pp. 255-61, https://doi.org/10.26415/2572-004X-vol2iss3p255-261
[21]. Carl B, Bop M, Voellger B, Saß B, Nimsky C (2019) Augmented reality in transsphenoidal surgery, World Neurosurgery. doi: 10.1016/j.wneu.2019.01.202. https://doi.org/10.1016/j.wneu.2019.01.202 PMid:30763743
[22]. Razmjooy N, Estrela VV, Loschi H. J., Fanfan W. (2019) A comprehensive survey of new meta-heuristic algorithms, Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing.
[23]. Hemanth, D.H. & Estrela V.V. (2017) Deep Learning for Image Processing Applications, IOS. ISBN: 978-1-61499-821-1 (print) | 978-1-61499-822-8 (online)
[24]. Razmjooy, N., & Estrela, V. V. (2019) Applications of Image Processing and Soft Computing Systems in Agriculture (pp. 1-337). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-8027-0 https://doi.org/10.4018/978-1-5225-8027-0
[25]. Estrela, V. V., & Herrmann, A. E. (2016) Content-based image retrieval (CBIR) in remote clinical diagnosis and healthcare. In M. Cruz-Cunha, I. Miranda, R. Martinho, & R. Rijo (Eds.), Encyclopedia of E-Health and Telemedicine (pp. 495-520). Hershey, PA: IGI https://doi.org/10.4018/978-1-4666-9978-6.ch039
[26]. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2016) SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615 PMid:28060704
[27]. Milletari, F., Navab, N., & Ahmadi, S. (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), 565-571. https://doi.org/10.1109/3DV.2016.79