Abstract
The cerebellum is an important structure to determine fetal development because its volume has a high correlation with gestational age. Manual annotation of the cerebellum in 3D ultrasound images (to measure the cerebellar volume) requires highly trained experts to perform a time-consuming task. To assist in this task, we developed a totally automatic system for the 3D segmentation of the cerebellum in ultrasound images of the fetal brain, using a 3D Point Distribution Model (PDM) obtained from another statistical shape model based on a spherical harmonics (SPHARMs) representation, which provides a very efficient basis for the construction of statistical shape models of 3D organs with a spherical topology. Our PDM of the fetal cerebellum was automatically adjusted with the optimization of an objective function based on gray level voxel profiles, using a genetic algorithm. An automatic initialization and plane selection scheme was also developed, based on the detection of the cerebellum on each plane by a convolutional neural network (YOLO v2). Our results of the 3D segmentation of 18 ultrasound volumes of the fetal brain are: Dice coefficient of 0.83 ± 0.10 and Hausdorff distance of 3.61 ± 0.83 mm. The methods reported show potential to successfully assist the experts in the assessment of fetal growth in ultrasound volumes.
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Acknowledgements
The financial support of UNAM (grants PAPIIT IV100420 and PAPIIT IA104622), CONAHCYT and “Programa de Becas Posdoctorales de DGAPA” is gratefully acknowledged. Fabian Torres acknowledges the support of the Postdoctoral Fellowship granted by CONAHCYT (CVU 298645). Zian Fanti and Gustavo Velásquez, gratefully acknowledge the support of CONACYT with their doctoral scholarships.
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GVR: Designed and implemented the algorithms, performed the tests and analysis of results. Collaborated in the preparation of the manuscript. ZFG and FTR: Collaborated in the design and implementation of the algorithms, provided feedback on the manuscript. VMB and BER: Provided feedback on the design of the algorithms, collaborated in the analysis of results and the writing of the manuscript. LCM and MGH: Cured and annotated all the ultrasound data, provided clinical guidance on the segmentation of the cerebellum, collaborated in the analysis of results and the writing of the manuscript. FAC: Collaborated in the design and implementation of the algorithms, test design and analysis of results. He also collaborated in the writing of the manuscript.
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Velásquez-Rodríguez, G.A., Fanti-Gutiérrez, Z., Torres, F. et al. 3D statistical shape models for automatic segmentation of the fetal cerebellum in ultrasound images. SIViP 19, 81 (2025). https://doi.org/10.1007/s11760-024-03615-1
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DOI: https://doi.org/10.1007/s11760-024-03615-1