TY - JOUR
T1 - Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI
AU - Alomari, Raja S.
AU - Corso, Jason J.
AU - Chaudhary, Vipin
AU - Dhillon, Gurmeet
PY - 2010/5
Y1 - 2010/5
N2 - Purpose Detection of abnormal discs from clinical T2-weighted MR Images. This aids the radiologist as well as subsequent CAD methods in focusing only on abnormal discs for further diagnosis. Furthermore, it gives a degree of confidence about the abnormality of the intervertebral discs that helps the radiologist in making his decision. Materials and methods We propose a probabilistic classifier for the detection of abnormality of intervertebral discs. We use three features to label abnormal discs that include appearance, location, and context. We model the abnormal disc appearance with a Gaussian model, the location with a 2D Gaussian model, and the context with a Gaussian model for the distance between abnormal discs.We infer on the middle slice of the T2-weighted MRI volume for each case. These MRI scans are specific for the lumbar area. We obtain our gold standard for the ground truth from our collaborating radiologist group by having the clinical diagnosis report for each case. Results We achieve over 91% abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds; in each round, we randomly leave 30 cases out for testing and we use the other 50 cases for training. Conclusion We achieve high accuracy for detection of abnormal discs using our proposed model that incorporates disc appearance, location, and context.We showthe extendability of our proposed model to subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation and herniation.
AB - Purpose Detection of abnormal discs from clinical T2-weighted MR Images. This aids the radiologist as well as subsequent CAD methods in focusing only on abnormal discs for further diagnosis. Furthermore, it gives a degree of confidence about the abnormality of the intervertebral discs that helps the radiologist in making his decision. Materials and methods We propose a probabilistic classifier for the detection of abnormality of intervertebral discs. We use three features to label abnormal discs that include appearance, location, and context. We model the abnormal disc appearance with a Gaussian model, the location with a 2D Gaussian model, and the context with a Gaussian model for the distance between abnormal discs.We infer on the middle slice of the T2-weighted MRI volume for each case. These MRI scans are specific for the lumbar area. We obtain our gold standard for the ground truth from our collaborating radiologist group by having the clinical diagnosis report for each case. Results We achieve over 91% abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds; in each round, we randomly leave 30 cases out for testing and we use the other 50 cases for training. Conclusion We achieve high accuracy for detection of abnormal discs using our proposed model that incorporates disc appearance, location, and context.We showthe extendability of our proposed model to subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation and herniation.
KW - Computer-aided diagnosis
KW - Gibbs distribution
KW - Lumbar intervertebral disc
KW - MRI
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U2 - 10.1007/s11548-009-0396-9
DO - 10.1007/s11548-009-0396-9
M3 - Article
AN - SCOPUS:77953615579
SN - 1861-6410
VL - 5
SP - 287
EP - 293
JO - International Journal of Computer Assisted Radiology and Surgery
JF - International Journal of Computer Assisted Radiology and Surgery
IS - 3
ER -