Literature DB >> 24351846

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

Daniel Forsberg1, Claes Lundström, Mats Andersson, Hans Knutsson.   

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 three-dimensional 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 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 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 a 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.

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Year:  2013        PMID: 24351846     DOI: 10.1088/0031-9155/59/2/311

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  3 in total

1.  Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.

Authors:  Brian H Cho; Deepak Kaji; Zoe B Cheung; Ivan B Ye; Ray Tang; Amy Ahn; Oscar Carrillo; John T Schwartz; Aly A Valliani; Eric K Oermann; Varun Arvind; Daniel Ranti; Li Sun; Jun S Kim; Samuel K Cho
Journal:  Global Spine J       Date:  2019-08-13

2.  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).

Authors:  Mariana Bustamante; Vikas Gupta; Carl-Johan Carlhäll; Tino Ebbers
Journal:  J Cardiovasc Magn Reson       Date:  2017-06-23       Impact factor: 5.364

3.  Biomechanical Morphing for Personalized Fitting of Scoliotic Torso Skeleton Models.

Authors:  Christos Koutras; Hamed Shayestehpour; Jesús Pérez; Christian Wong; John Rasmussen; Maxime Tournier; Matthieu Nesme; Miguel A Otaduy
Journal:  Front Bioeng Biotechnol       Date:  2022-07-19
  3 in total

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