Purpose: Detection of abnormal discs from clinical T2weighted MR scans. 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. Material and Methods: We propose a probabilistic model for detection of abnormality of intervertebral discs. We use three features to label abnormal discs that includes 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 use clinical T2-weighted MR volume for each case and inference on the middle slide of each volume. These MR scans are specific for the lumbar area. The ground truth is provided by our collaborating radiologist. Results: We achieve over 91 % abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds, in every round 30 cases are randomly left out for testing and the rest are used 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 that our proposed model is extensible for subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation, stenosis, and herniation
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.