Computer assisted detection (CAD) of lymph node metastases may help reduce reading time and improve interpretation of the large amount of image data in an MR-lymphography exam. We compared the influence of using different segmentation methods on the performance of a CAD system for classification of normal and metastasized lymph nodes. Our database consisted of T1 and T2-weighted pelvic MR images of 603 lymph nodes, enhanced by USPIO contrast medium. For each lymph node, one seed point was manually defined Three automated segmentation methods were compared: 1. Confidence Connected segmentation, extended with automated Bandwidth Factor selection; 2. Conventional Graph Cut segmentation; 3. Pseudo-segmentation by selecting a sphere around the seed point. All lymph nodes were also manually segmented by a radiologist. The resulting segmentations were used to calculate 2 features (mean T1 and T2 signal intensity). Linear discriminant analysis was used for classification. The diagnostic accuracy (AUC at ROC-analysis) was: 0.95 (Confidence- Connected); 0.95 (Graph-Cut); 0.85 (spheres); and 0.95 (manual segmentations). The CAD performance of both the Confidence Connected and Graph Cut methods was as good as the manual segmentation. The substantially lower performance of the sphere segmentations demonstrates the need for accurate segmentations, even in USPIO-enhanced images.
Automated classification of lymph nodes in USPIO-enhanced MR-images: a comparison of three segmentation methods
O. Debats, N. Karssemeijer, J. Barentsz and H. Huisman
Medical Imaging 2010;7624:76240Q.