Automated identification of spinal cord and vertebras on sagittal MRI

Chuan Zhou; Heang-Ping Chan; Qian Dong; Bo He; Jun Wei; Lubomir M. Hadjiiski; Daniel Couriel

doi:10.1117/12.2043500

Abstract

We are developing an automated method for the identification of the spinal cord and the vertebras on spinal MR images, which is an essential step for computerized analysis of bone marrow diseases. The spinal cord segment was first enhanced by a newly developed hierarchical multiscale tubular (HMT) filter that utilizes the complementary hyper- and hypo- intensities in the T1-weighted (T1W) and STIR MRI sequences. An Expectation-Maximization (EM) analysis method was then applied to the enhanced tubular structures to extract candidates of the spinal cord. The spinal cord was finally identified by a maximum-likelihood registration method by analysis of the features extracted from the candidate objects in the two MRI sequences. Using the identified spinal cord as a reference, the vertebras were localized based on the intervertebral disc locations extracted by another HMT filter applied to the T1W images. In this study, 5 and 30 MRI scans from 35 patients who were diagnosed with multiple myeloma disease were collected retrospectively with IRB approval as training and test set, respectively. The vertebras manually outlined by a radiologist were used as reference standard. A total of 422 vertebras were marked in the 30 test cases. For the 30 test cases, 100% (30/30) of the spinal cords were correctly segmented with 4 false positives (FPs) mistakenly identified on the back muscles in 4 scans. A sensitivity of 95.0% (401/422) was achieved for the identification of vertebras, and 5 FPs were marked in 4 scans with an average FP rate of 0.17 FPs/scan.

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Chuan Zhou, Chuan Zhou, Heang-Ping Chan, Heang-Ping Chan, Qian Dong, Qian Dong, Bo He, Bo He, Jun Wei, Jun Wei, Lubomir M. Hadjiiski, Lubomir M. Hadjiiski, Daniel Couriel, Daniel Couriel, "Automated identification of spinal cord and vertebras on sagittal MRI", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903515 (24 March 2014); doi: 10.1117/12.2043500; https://doi.org/10.1117/12.2043500

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