Automated quasi-3D spine curvature quantification and classification

Rupal Khilari; Juris Puchin; Kazunori Okada

doi:10.1117/12.2293293

27 February 2018 Automated quasi-3D spine curvature quantification and classification

Rupal Khilari, Juris Puchin, Kazunori Okada

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105753C (2018) https://doi.org/10.1117/12.2293293
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Scoliosis is a highly prevalent spine deformity that has traditionally been diagnosed through measurement of the Cobb angle on radiographs. More recent technology such as the commercial EOS imaging system, although more accurate, also require manual intervention for selecting the extremes of the vertebrae forming the Cobb angle. This results in a high degree of inter and intra observer error in determining the extent of spine deformity. Our primary focus is to eliminate the need for manual intervention by robustly quantifying the curvature of the spine in three dimensions, making it consistent across multiple observers. Given the vertebrae centroids, the proposed Vertebrae Sequence Angle (VSA) estimation and segmentation algorithm finds the largest angle between consecutive pairs of centroids within multiple inflection points on the curve. To exploit existing clinical diagnostic standards, the algorithm uses a quasi-3-dimensional approach considering the curvature in the coronal and sagittal projection planes of the spine. Experiments were performed with manuallyannotated ground-truth classification of publicly available, centroid-annotated CT spine datasets. This was compared with the results obtained from manual Cobb and Centroid angle estimation methods. Using the VSA, we then automatically classify the occurrence and the severity of spine curvature based on Lenke’s classification for idiopathic scoliosis. We observe that the results appear promising with a scoliotic angle lying within ± 9° of the Cobb and Centroid angle, and vertebrae positions differing by at the most one position. Our system also resulted in perfect classification of scoliotic from healthy spines with our dataset with six cases.

Citation Download Citation

Rupal Khilari, Juris Puchin, and Kazunori Okada "Automated quasi-3D spine curvature quantification and classification", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753C (27 February 2018); https://doi.org/10.1117/12.2293293

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KEYWORDS

Spine

Radiography

Classification systems

Computed tomography

3D image processing

Detection and tracking algorithms

Computer science

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