Model-based registration for assessment of spinal deformities in idiopathic scoliosis

Abstract

Abstract. Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full 3D spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5 • ± 1.8, which is on par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

“…Regarding the MSD, in Table can be appreciated that this measure was clearly improved after applying the atlas. A mean value of 0.66 ± 0.25 was obtained for the whole spine, better value than the obtained in previous works . The results reported by Korez et al and Castro‐Mateos et al were more accurate in the thoracic region but, to evaluate this region, they only included healthy patients.…”

“…However, many of these works either do not segment all thoracic and lumbar vertebrae, or they are not completely automatic, or have been only tested in healthy cases . The presence of the ribs is one of the problems for which different methods show less accuracy in the segmentation of the thoracic region . This is mainly due to the difficulty of discriminating between these structures and the vertebrae.…”

“…18,25 The presence of the ribs is one of the problems for which different methods show less accuracy in the segmentation of the thoracic region. 16,21,[27][28][29] This is mainly due to the difficulty of discriminating between these structures and the vertebrae. However, most of the problems leading to back pain are related to lumbar region, as this area supports the greatest load of the spine.…”

Improving visualization of 4D flow cardiovascular magnetic resonance with four-dimensional angiographic data: generation of a 4D phase-contrast magnetic resonance CardioAngiography (4D PC-MRCA)

“…The implementation uses diffeomorphic field accumulation, together with fluid and elastic regularization of the displacement fields in order to generate physically plausible deformations [14]. The regularization parameters were initially determined following the settings from a previous study where the same registration method was used on clinical computed tomography (CT) images [15]. Further tuning of these parameters for the current application resulted in the following settings:

Number of scales: 2

Number of iterations per scale: 3

Gaussian kernel with σ of 1.5 pixels for both fluid and elastic regularization.

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Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Preliminary results

“…Regarding atlases of the vertebral bodies, most of them are centered in the lumbar region and the algorithms developed are semi-automatic [13], being necessary some degree of user interaction to align the atlas with the scan of interest [14], to label points for a reference model [7] or to select points to identify the vertebrae [15]. Forsberg et al [16] developed a fully automatic method covering thoracic and lumbar vertebrae. However, they focused their study only on idiopathic scoliosis estimating position and rotation of each vertebra in the spine.…”