Velásquez RG, Arámbula CF, Guzmán HME, Camargo ML, Borboa OH, Escalante RB

Language: English
References: 12
Page: 121-129
PDF size: 2371.61 Kb.

ABSTRACT

The size of the cerebellum in ultrasound volumes of the fetal brain has shown a high correlation with gestational age, which makes it a valuable feature to detect fetal growth restrictions. Manual annotation of the 3D surface of the cerebellum in an ultrasound volume is a time consuming task, which needs to be performed by a highly trained expert. In order to assist the experts in the evaluation of cerebellar dimensions, we developed an automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model. In this work we present our validation results on 10 ultrasound volumes in which we have obtained an adequate accuracy in the segmentation of the cerebellum (mean Dice coefficient of 0.689). The method reported shows potential to effectively assist the experts in the assessment of fetal growth in ultrasound volumes.

REFERENCES

1. E. A. Júnior, H.A. Guimarães Filho, C.R. Pires, L.M. Nardozza, A.F. Moron, & R. Mattar, “Validation of fetal cerebellar volume by threedimensional ultrasonography in Brazilian population,” Archives of Gynecology and Obstetrics, vol. 275, no. 1, pp. 5-11, 2007.

2. A. Benavides-Serralde, E. Hernández- Andrade, J. Fernández-Delgado, W. Plasencia, M. Scheier, F. Crispi, ... & E. Gratacos, “Threedimensional sonographic calculation of the volume of intracranial structures in growth-restricted and appropriate for gestational age fetuses,” Ultrasound in Obstetrics & Gynecology, vol. 33, no. 5, pp. 530-537, 2009.

3. T. Hata, A. Kuno, S.Y. Dai, E. Inubashiri, U. Hanaoka, K. Kanenishi, ... & T. Yanagihara, “Threedimensional sonographic volume measurement of the fetal spleen,” Journal of Obstetrics and Gynecology Research, vol. 33, no. 5, pp. 600-605, 2007.

4. C.H. Chang, C.H. Yu, F.M. Chang, H.C. Ko, & H.Y. Chen, “The assessment of normal fetal brain volume by 3-D ultrasound,” Ultrasound in Medicine & Biology, vol. 29, no. 9, pp. 1267-1272, 2003.

5. B. Gutiérrez-Becker, F. Arámbula Cosío, M.E. Guzmán Huerta, J.A. Benavides-Serralde, L. Camargo-Marín, & V. Medina Bañuelos, “Automatic segmentation of the fetal cerebellum in ultrasound volumes, using a 3D statistical shape model,” Medical & Biological Engineering & Computing, vol. 51, no. 9, pp. 1021-1030, 2013.

6. W.E. Lorensen, & H.E. Cline, “Marching cubes: A high resolution 3D surface construction algorithm,” ACM Siggraph Computer Graphics, vol. 21, no. 4, pp. 163-169, 1987.

7. L. Shen, H. Farid, & M.A. McPeek, “Modeling three-dimensional morphological structures using spherical harmonics,” Evolution, vol. 63, no. 4, pp. 1003-1016, 2009.

8. S.A. Ahmadi, M. Baust, A. Karamalis, A. Plate, K. Boetzel, T. Klein, & N. Navab, “Midbrain segmentation in transcranial 3d ultrasound for Parkinson diagnosis,” In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011, pp. 362- 369. Springer Berlin Heidelberg, 2011.

9. L.R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297-302, 1945.

10. Y. Yu, J.A. Molloy, & S.T. Acton, “Three-dimensional speckle reducing anisotropic diffusion,” In Signals, Systems and Computers, 2004. Conference Record of the Thirty- Seventh Asilomar Conference on, vol. 2, pp. 1987-1991, IEEE, 2003.

11. P.J. Besl, N.D. McKay, “A method for registration of 3-D Shapes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, 1992.

12. M. Yaqub, R. Cuignet, R. Napolitano, D. Roundhill, A. Papageorghiou, R. Ardon, J.A. Noble, “Volumetric segmentation of key fetal brain structures in 3D ultrasound,” In Machine Learning in Medical Imaging 2013, LNCS 8184, pp. 25-32, 2013.