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Purpose: To develop a fully automated method to segment cartilage from the magnetic resonance (MR) images of knee and to evaluate the performance of the method on a public, open dataset. Methods: The segmentation scheme consisted of three procedures: multiple-atlas building, applying a locally weighted vote (LWV), and region adjustment. In the atlas building procedure, all training cases were registered to a target image by a nonrigid registration scheme and the best matched atlases selected. A LWV algorithm was applied to merge the information from these atlases and generate the initial segmentation result. Subsequently, for the region adjustment procedure, the statistical information of bone, cartilage, and surrounding regions was computed from the initial segmentation result. The statistical information directed the automated determination of the seed points inside and outside bone regions for the graph-cut based method. Finally, the region adjustment was conducted by the revision of outliers and the inclusion of abnormal bone regions. Results: A total of 150 knee MR images from a public, open dataset (available at www.ski10.org) were used for the development and evaluation of this approach. The 150 cases were divided into the training set (100 cases) and the test set (50 cases). The cartilages were segmented successfully in all test cases in an average of 40 min computation time. The average dice similarity coefficient was 71.7% ± 8.0% for femoral and 72.4% ± 6.9% for tibial cartilage. Conclusions:The authors have developed a fully automated segmentation program for knee cartilage from MR images. The performance of the program based on 50 test cases was highly promising.

ObjectiveTo determine the utility of perfusion MR imaging in the differential diagnosis of brain tumors.Materials and MethodsFifty-seven patients with pathologically proven brain tumors (21 high-grade gliomas, 8 low-grade gliomas, 8 lymphomas, 6 hemangioblastomas, 7 metastases, and 7 various other tumors) were included in this study. Relative cerebral blood volume (rCBV) and time-to-peak (TTP) ratios were quantitatively analyzed and the rCBV grade of each tumor was also visually assessed on an rCBV map.ResultsThe highest rCBV ratios were seen in hemangioblastomas, followed by high-grade gliomas, metastases, low-grade gliomas, and lymphomas. There was no significant difference in TTP ratios between each tumor group (p>0.05). At visual assessment, rCBV was high in 17 (81%) of 21 high-grade gliomas and in 4 (50%) of 8 low-grade gliomas. Hemangioblastomas showed the highest rCBV and lymphomas the lowest.ConclusionPerfusion MR imaging may be helpful in the differentiation of thevarious solid tumors found in the brain, and in assessing the grade of the various glial tumors occurring there.