Automatic segmentation of the fetal cerebellum using spherical harmonics and gray level profiles

Gustavo Velásquez-Rodríguez; Fernando Arámbula Cosío; Boris Escalate Ramírez

doi:10.1117/12.2207833

22 December 2015 Automatic segmentation of the fetal cerebellum using spherical harmonics and gray level profiles

Gustavo Velásquez-Rodríguez, Fernando Arámbula Cosío, Boris Escalate Ramírez

Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 968114 (2015) https://doi.org/10.1117/12.2207833
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

Abstract

The cerebellum is an important structure to determine the gestational age, cerebellar diameter obtained by ultrasound volumes of the fetal brain has shown a high correlation with gestational age, therefore is useful to determine fetal growth restrictions. The manual annotation of 3D surfaces from the fetal brain is time consuming and needs to be done by a highly trained expert. To help with the annotation in the evaluation of cerebellar diameter, we developed a new automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model and the optimization of an objective function based on gray level voxel profiles. The results on 10 ultrasound volumes of the fetal brain show an accuracy in the segmentation of the cerebellum (mean Dice coefficient of 0.7544). The method reported shows potential to effectively assist the experts in the assessment of fetal growth in ultrasound volumes. We consider the proposed cerebellum segmentation method a contribution for the SPHARM segmentations models, because it is capable to run without hardware restriction, (GPU), and gives adequate results in a reasonable amount of time.

Citation Download Citation

Gustavo Velásquez-Rodríguez, Fernando Arámbula Cosío, and Boris Escalate Ramírez "Automatic segmentation of the fetal cerebellum using spherical harmonics and gray level profiles", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968114 (22 December 2015); https://doi.org/10.1117/12.2207833

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