Cardiac Segmentation Using Variable Scale Statistics

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Presentation transcript:

Cardiac Segmentation Using Variable Scale Statistics C. A. Cois, George Stetten, John Galleotti, Michael Sacks (University of Pittsburgh, CMU) Danielle Gottlieb, MD, Andrew J. Powell, MD, John E. Mayer, Jr., MD (Children’s Hospital, Boston) Mei Chen (Intel Research Pittsburgh) Introduction Segmentation: Process of partitioning an image into regions Goal: Provide a clinically accessible system for automated medical segmentation with minimal supervision Results Aortic Arch (A) and Right Heart (B) segmentations from real CT and MRI data, respectively Method User initializes by tracing the target object in a single 2D slice System extracts seed points and statistical information from tracing System automatically optimizes algorithm parameters for 3D analysis & segmentation System places spherical operators at each voxel and adjusts their radii to meet the nearest object boundary System determines medial spheres and segments using a connected flood-fill. Validation Validated using 3D expert segmentations of the Right Ventricular Outflow Tract (RVOT) in 10 MRI data sets Compared to state of the art Geodesic Active Contours Dice Similarity Coefficient (DSC) measures segmentation agreement Supported by the National Library of Medicine and the Cardiovascular Bioengineering Training Program, NIHT32-HL76124."