1. 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 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."