Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans

Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations

Multiple Fuzzy Object Modeling Improves Sensitivity In Automatic Anatomy Recognition

Computerized automatic anatomy recognition (AAR) is an essential step for implementing body-wide quantitative radiology (QR). Our strategy to automatically identify and delineate various organs in a given body region is based on fuzzy models and an organ hierarchy. In previous years, the basic algorithms of our AAR approach - model building, recognition, and delineation - and their evaluation were presented. In the present paper, we propose to replace the single fuzzy model built for each organ by a set of fuzzy models built for the same organ. Based on a dataset composed of CT images of the Thorax region of 50 subjects, our experiments indicate that recognition performance improves when using multiple models instead of a single model for each organ. It is interesting to point out that the improvement is not uniform for all organs, leading us to conclude that some organs will benefit from the multiple model approach more than others. © 2014 SPIE.9034Intrace Medical,Modus Medical Devices Inc.,The Society of Photo-Optical Instrumentation Engineers (SPIE),Ventana Medical Systems Inc.,XIFIN, IncUdupa, J.K., Odhner, D., Falcao, A.X., Ciesielski, K.C., Miranda, P.A.V., Vaideeswaran, P., Mishra, S., Torigian, D.A., Fuzzy object modeling (2011) Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, pp. 79640BUdupa, J.K., Odhner, D., Falcao, A.X., Ciesielski, K.C., Miranda, P.A.V., Matsumoto, M., Grevera, G.J., Torigian, D.A., Automatic anatomy recognition via fuzzy object models (2012) Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, p. 831605Udupa, J.K., Odhner, D., Zao, L., Tong, Y., Matsumoto, M.M.S., Ciesielski, K.C., Falcao, A.X., Torigian, D.A., Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images Medical Image Analysis, , submittedWard Jr., J.H., Hierarchical grouping to optimize an objective function (1963) Journal of the American Statistical Association, 58, pp. 236-24