Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's initial visit is a challenging task. This research provides a bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific (PS) parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework, taking dynamic aspects into account. The proposed technique is being utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis (AIS). The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow (GVF) snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. The snake parameters are calibrated on the dataset, resulting in considerable improvement in image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point (ICP) mesh registration technique is then used to establish a mesh morphing approach and creates a 3D Atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. The proposed bio-informed deep learning network with the modified bone growth model not only significantly outperforms other state-of-the-art model-based methods, but also achieves competitive or even superior performance against state-of-the-art learning-based methods.

Creator

• Tajdari, Mahsa

DOI

• https://doi.org/10.21985/n2-0d3r-9m64

Subject

• Mechanical engineering
• Biomechanics

Language

• en

Alternate Identifier

• etdadmin_upload_879756
• http://dissertations.umi.com/northwestern:15944

Keyword

• Adolescent idiopathic scoliosis (AIS)
• Mechanistic bio-informed machine learning
• Pediatric spinal deformity (PSD)
• 3D reconstruction
• X-ray images
• Mesh registration

Date created

• 2022-01-01

Resource type

• Dissertation

Rights statement

• In Copyright