Name
Abstract | ||
|---|---|---|
Degenerative disc disease (DDD) can be identified as hyperdense regions of bone and osseous spur formation in the spine that become more prevalent with age. These regions can act as confounding factors in the search for alternative hyperdense foci such as neoplastic processes. We created a preliminary CAD system that detects DDD in the spine on CT images. After the spine is segmented, the cortical shell of each vertebral body is unwrapped onto a 2D map. Candidates are detected from the 2D map based on their intensity and gradient. The 2D detections are remapped into 3D space and a level set algorithm is applied to more fully segment the 3D lesions. Features generated from the unwrapped 2D map and 3D segmentation are combined to train a support vector machine (SVM) classifier. The classifier was trained on 20 cases with DDD, which were marked by a radiologist. The pre-SVM program detected 164/193 ground truth lesions. Preliminary results showed 69.65% sensitivity with a 95% confidence interval of (64.47%, 73.92%), at an average of 9.8 false positives per patient. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2008063 | Proceedings of SPIE |
Keywords | Field | DocType |
degenerative disc disease,osteophytes,computer-aided detection | Computer vision,Focus (geometry),Segmentation,Neoplastic Processes,Support vector machine,Artificial intelligence,Radiology,Cad system,Confidence interval,Degenerative disc disease,False positive paradox,Physics | Conference |
Volume | ISSN | Citations |
8670 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| hector munoz | 1 | 0 | 0.68 |
| Jianhua Yao | 2 | 1135 | 110.49 |
| Joseph E. Burns | 3 | 89 | 9.51 |
| Ronald M. Summers | 4 | 893 | 86.16 |