Finite element models with automatic computed tomography bone segmentation for failure load computation

• Published in: Scientific reports Vol. 14; no. 1; pp. 16576 - 9
• Main Authors: Saillard, Emile, Gardegaront, Marc, Levillain, Aurélie, Bermond, François, Mitton, David, Pialat, Jean-Baptiste, Confavreux, Cyrille, Grenier, Thomas, Follet, Hélène
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 631/114/2397; 631/67/1344; 631/67/322; 631/67/322/803; 692/499; Biomechanical Phenomena; Biomechanics; Bone and Bones - diagnostic imaging; Computed tomography; Computer Science; Computer Simulation; Deep Learning; Engineering Sciences; Femur - diagnostic imaging; Finite Element Analysis; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Mathematical models; Mechanics; Medical Imaging; Metastases; multidisciplinary; Science; Science (multidisciplinary); Simulation; Spine; Spine - diagnostic imaging; Tomography, X-Ray Computed - methods; Vertebrae; metastasis, risk factors, Bone cancer, Bone metastases, Computational models, image processing, machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-66934-w

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedious and time consuming task when done manually, and expert segmentations are subject to intra- and inter-operator variability. Deep learning methods are increasingly employed to automatically carry out image segmentation tasks. These networks usually need to be trained on a large image dataset along with the manual segmentations to maximize generalization to new images, but it is not always possible to have access to a multitude of CT-scans with the associated ground truth. It then becomes necessary to use training techniques to make the best use of the limited available data. In this paper, we propose a dedicated pipeline of preprocessing, deep learning based segmentation method and post-processing for in-vivo human femurs and vertebrae segmentation from CT-scans volumes. We experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation if carefully trained. We compared the failure load simulation results obtained on femurs and vertebrae using either automatic or manual segmentations and studied the sensitivity of the simulations on small variations of the automatic segmentation. The failure loads obtained using automatic segmentations were comparable to those obtained using manual expert segmentations for all the femurs and vertebrae tested, demonstrating the effectiveness of the automated segmentation approach for failure load simulations.