Article Dans Une Revue International Journal of Biomedical Engineering and Technology (IJBET) Année : 2024

Automatic part segmentation for full newborn skeleton-articulated geometries using geometric deep learning and 3D point cloud

Résumé

The development of the maternal pelvis model including a detailed foetal model with articulated joints is of great clinical relevance. The objective of the present study is to propose an automatic and fast segmentation workflow of the full newborn skeleton using geometric deep learning. Computed tomography scans of 124 newborn were retrieved and manually segmented. PointNet++, a geometric deep learning algorithm, was trained to perform automatic segmentation on the 3D point clouds of the reconstructed skeletons. This method was compared with the k-means clustering approach. The PointNet++ model provided highly accurate results, with an accuracy of 95.7 ± 4.7% and an IoU of 91.7 ± 7.9%, while k-means clustering provided unsatisfactory results (Accuracy = 74.6 ± 3.7% and IoU = 59.8 ± 4.7%). This study provided a powerful and accurate automatic segmentation workflow for the full newborn skeleton.

Fichier principal
Vignette du fichier
Ferrandini_IJBET2023_HAL.pdf (2.95 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05241517 , version 1 (05-09-2025)

Licence

Identifiants

Citer

Morgane Ferrandini, Duc-Phong Nguyen, Duyen Hien Le Nguyen, Vi Do Tran, Hoai Danh Vo, et al.. Automatic part segmentation for full newborn skeleton-articulated geometries using geometric deep learning and 3D point cloud. International Journal of Biomedical Engineering and Technology (IJBET), 2024, 46 (2), pp.101-115. ⟨10.1504/IJBET.2024.141577⟩. ⟨hal-05241517⟩
77 Consultations
108 Téléchargements

Altmetric

Partager

  • More