Abstract
Segmentation of the fetal heart can facilitate the 3D assessment of the cardiac function and structure. Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. This paper presents a level set deformable model to simultaneously segment all four cardiac chambers using region based information. The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm’s delineation and manual tracings are within 7 pixels (<2mm) in 2D and under 3 voxels (<4.5mm) in 3D. The ejection fraction was determined from the 3D dataset. Future work will include further testing on additional datasets and validation on a phantom.
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References
Mitchell, S.C., Korones, S.B., Berendes, H.W.: Congenital heart disease in 56,109 births. Incidence and natural history. Circulation 43, 323–332 (1971)
Copel, J.A., Gianluigi, P., Green, J., Hobbins, J.C., Kleinman, C.S.: Fetal echocardiographic screening for congenital heart disease: The importance of the four-chamber view. British Journal of Obstetrics and Gynaecology 157, 648–655 (1987)
Esh-Broder, E., Ushakov, F.B., Imbar, T., Yagel, S.: Application of free-hand three-dimensional echocardiography in the evaluation of fetal cardiac ejection fraction: a preliminary study. Ultrasound in Obstetrics & Gynecology 23, 546–551 (2004)
Deng, J.: Terminology of three-dimensional and four-dimensional ultrasound imaging of the fetal heart and other moving parts. Ultrasound in Obstetrics & Gynecology 22, 336–334 (2003)
Piccoli, L., Dahmer, A., Scharcanski, J., Navaux, P.O.A.: Fetal echocardiographic image segmentation using neural networks. In: Seventh International Conference on Image Processing and its Applications 1999 (1999)
Siqueira, M.L., Scharcanski, J., Navaux, P.O.A.: Echocardiographic image sequence segmentation and analysis using self-organizing maps. Journal of VLSI Signal Processing 32, 135–145 (2002)
Lassige, T.A., Benkeser, P.J., Fyfe, D., Sharma, S.: Comparison of septal defects in 2D and 3D echocardiography using active contour models. Computerized Medical Imaging and Graphics 24, 377–388 (2000)
Dindoyal, I., Lambrou, T., Deng, J., Ruff, C.F., Linney, A.D., Todd-Pokropek, A.: An active contour model to segment foetal cardiac ultrasound data. In: Medical Image Understanding and Analysis, University of Sheffield, UK, July 10 (2003)
Deng, J., Ruff, C.F., Linney, A.D., Lees, W.R., Hanson, M.A., Rodeck, C.H.: Simultaneous use of two ultrasound scanners for motion-gated three-dimensional fetal echocardiography. Ultrasound in Medicine and Biology 26, 1021–1032 (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1, 321–331 (1988)
Sarti, A.: Subjective surfaces: a geometric model for boundary completion. International Journal of Computer Vision 46, 201–221 (2002)
Gibou, F., Fedkiw, R.: A fast hybrid k-means level set algorithm for segmentation. Stanford Technical Report (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Dindoyal, I. et al. (2005). Level Set Segmentation of the Fetal Heart. In: Frangi, A.F., Radeva, P.I., Santos, A., Hernandez, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2005. Lecture Notes in Computer Science, vol 3504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494621_13
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DOI: https://doi.org/10.1007/11494621_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26161-2
Online ISBN: 978-3-540-32081-4
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