A freehand ultrasound framework for spine assessment in 3D: a preliminary study.

X-ray imaging is currently the gold standard for the assessment of spinal deformities. The purpose of this study is to evaluate a freehand 3D ultrasound system for volumetric reconstruction of the spine. A setup consisting of an ultrasound scanner with a linear transducer, an electromagnetic measuring system and a workstation was used. We conducted 64 acquisitions of US images of 8 adults in a natural standing position, and we tested three setups: 1) Subjects are constrained to be close to a wall, 2) Subjects are unconstrained, and 3) Subjects are constrained to performing fast and slow acquisitions. The spinous processes were manually selected from the volume reconstruction from tracked ultrasound images to generate a 3D point-based model depicting the centerline of the spine. The results suggested that a freehand 3D ultrasound system can be suitable for representing the spine. Volumetric reconstructions can be computed and landmarking can be performed to model the surface of the spine in the 3D space. These reconstructions promise to generate computer-based descriptors to analyze the shape of the spine in the 3D space.Clinical Relevance- We provide clinicians with a protocol that could be integrated in clinical setups for the assessment and monitoring of AIS, based on US image acquisitions, which constitutes a radiation-free technology.

Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.

BACKGROUND: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provide the right treatment to prevent curve progression. METHOD: In this work, we propose a novel approach based on a statistical generative model to predict the shape variation of the spinal curve from the first visit. A spinal curve progression approach is learned using 3D spine models generated from retrospective biplanar X-rays. The prediction is performed every three months from the first visit, for a time lapse of one year and a half. An Independent Component Analysis (ICA) was computed to obtain Independent Components (ICs), which are used to describe the main directions of shape variations. A dataset of 3D shapes of 150 patients with AIS was employed to extract the ICs, which were used to train our approach. RESULTS: The approach generated an estimation of the shape of the spine through time. The estimated shape differs from the real curvature by 1.83, 5.18, and 4.79° of Cobb angles in the proximal thoracic, main thoracic, and thoraco-lumbar lumbar sections, respectively. CONCLUSIONS: The results obtained from our approach indicate that predictions based on ICs are very promising. ICA offers the means to identify the variation in the 3D space of the evolution of the shape of the spine. Another advantage of using ICs is that they can be visualized for interpretation.

Dynamic ensemble selection of learner-descriptor classifiers to assess curve types in adolescent idiopathic scoliosis.

While classification is important for assessing adolescent idiopathic scoliosis (AIS), it however suffers from low interobserver and intraobserver reliability. Classification using ensemble methods may contribute to improving reliability using the proper 2D and 3D images of spine curvature features. In this study, we present two new techniques to describe the spine, namely, leave-one-out and fan leave-one-out. Using these techniques, three descriptors are computed from a stereoradiographic 3D reconstruction to describe the relationship between a vertebra and its neighbors. A dynamic ensemble selection method is introduced for automatic spine classification. The performance of the method is evaluated on a dataset containing 962 3D spine models categorized according to three curve types. With a log loss of 0.5623, the dynamic ensemble selection outperforms voting and stacking ensemble learning techniques. This method can improve intraobserver and interobserver reliability, identify the best combination of descriptors for characterizing spine curve types, and provide assistance to clinicians in the form of information to classify borderline curvature types. Graphical abstract ᅟ.