Multi atlas-based muscle segmentation in abdominal CT images with varying field of view

The development of automatic techniques for the analysis of abdominal CT images is a topic of large interest. By using automatic techniques, objective diagnostic support can be provided to physicians and organ segmentation can eliminate time-consuming manual procedures such as delineation. Automatic kidney segmentation has been achieved for healthy cases but is unsuccessful in cases with diseased kidney. In this paper we propose an automatic system to assist the segmentation of abdominal organs, using the medially positioned psoas major muscles’ shape and location along with previously accomplished segmentations of the liver and spleen. A framework is employed to segment the vertebral column and rib bones, and the left and right psoas major muscles are segmented using a multi-atlas-based segmentation with weighted decision fusion and non-rigid registration. Due to a varying field of view (FOV) in each dataset and the requirement of an equal FOV for registration, an adjustment is made between pairs of datasets using an automatic vertebra identification framework created in this paper. The vertebra identification shows desired results in 88% of 68 datasets. The psoas major segmentation accuracy is inspected using a cross-validation among 21 datasets, showing a median Jaccard similarity coefficient (JSC) of 63.4% and 68.6% for the left and right muscles respectively. Future work will focus on adapting the kidney segmentation framework to include the shape and position of the psoas major muscle in the processing.