Statistical shape analysis of gravid uteri throughout pregnancy by a ray description technique

Verwaerde, Jolanthe; Laforêt, Jérémy; Marque, Catherine; Rassineux, Alain

doi:10.1007/s11517-021-02402-1

Cited by 3 publications

(1 citation statement)

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“…This makes it challenging to use these models to characterize shape variations in pregnant women. Even though there are pregnancy-related shape models that use MRI or ultrasound data to characterize specific structures, such as the uterus or the pelvic floor [26], [27], there is a strong need for a statistical model that characterizes the morphological shape of pregnant women using 3D scans.…”

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confidence: 99%

Fast and Simple Statistical Shape Analysis of Pregnant Women Using Radial Deformation of a Cylindrical Template

Nayak,

Gebremariam,

Paljug

et al. 2024

IEEE Access

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Non-rigid deformation of a template to fit 3D scans of human subjects is widely used to develop statistical models of 3D human shapes and poses. Complex optimization problems must be solved to use these models to parameterize scans of pregnant women, thus limiting their use in antenatal point-of-care tools in low-resource settings. Moreover, these models were developed using datasets that did not contain any 3D scans of pregnant women. In this study, we developed a statistical shape model of the torso of pregnant women at greater than 36 weeks of gestation using fast and simple vertex-based deformation of a cylindrical template constrained along the radial direction. The 3D scans were pre-processed to remove noisy outlier points and segment the torso based on anatomical landmarks. A cylindrical template mesh T was then fitted onto the segmented scan of the torso by moving each vertex of T in the direction of the radial vector. This process is computationally inexpensive taking only 14.80 seconds to deform a template with 9090 vertices. Principal component analysis (PCA) was performed on the deformed vertex co-ordinates to find the directions of maximum variance. The first 10 principal vectors of our model explained 79.03% of the total variance and reconstructed unseen scans with a mean error of 2.43 cm. We also used the PCA weights of the first 10 principal vectors to accurately predict anthropometric measurements of the pregnant women.

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confidence: 99%