Segmentation of Vertebral Bodies in MR Images

Abstract

Segmentation of vertebral bodies is useful for diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures. In this paper, we present a fast and semi-automatic approach for spine segmentation in routine clinical MR images. Segmenting a single vertebra is based on multiple-feature boundary classification and mesh inflation, and starts with a simple point-in-vertebra initialization. The inflation retains a star-shape geometry to prevent selfintersections and uses a constrained subdivision hierarchy to control smoothness. Analyzing the shape of the first vertebra, the main spine direction is deduced and the locations of neighboring vertebral bodies are estimated for further segmentation. The method was tested on 11 routine lumbar datasets with 92 reference vertebrae resulting in a detection rate of 93%. The average Dice Similarity Coefficient (DSC) against manual reference segmentations was 78%, which is on par with state of the art. The main advantages of our method are high speed and a low amount of user interaction.