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 show the extendability of our proposed model to subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation and herniation.
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 show the extendability of our proposed model to subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation and herniation.
Authors: Howard S An; Paul A Anderson; Victor M Haughton; James C Iatridis; James D Kang; Jeffrey C Lotz; Raghu N Natarajan; Theodore R Oegema; Peter Roughley; Lori A Setton; Jill P Urban; Tapio Videman; Gunnar B J Andersson; James N Weinstein Journal: Spine (Phila Pa 1976) Date: 2004-12-01 Impact factor: 3.468
Authors: Daniel S Mulconrey; Reginald Q Knight; James D Bramble; Subhash Paknikar; Patrick A Harty Journal: Spine J Date: 2006-01-27 Impact factor: 4.166
Authors: Sofia K Michopoulou; Lena Costaridou; Elias Panagiotopoulos; Robert Speller; George Panayiotakis; Andrew Todd-Pokropek Journal: IEEE Trans Biomed Eng Date: 2009-04-14 Impact factor: 4.538
Authors: A Neubert; J Fripp; C Engstrom; D Walker; M-A Weber; R Schwarz; S Crozier Journal: J Am Med Inform Assoc Date: 2013-06-27 Impact factor: 4.497